An Intelligent IoT Sensing System for Rail Vehicle Running States Based on TinyML

Real-time identification of the running state is one of the key technologies for a smart rail vehicle. However, it is a challenge to accurately real-time sense the complex running states of the rail vehicle on an Internet-of-Things (IoT) edge device. Traditional systems usually upload a large amount of real-time data from the vehicle to the cloud for identification, which is laborious and inefficient. In this paper, an intelligent identification method for rail vehicle running state is proposed based on Tiny Machine Learning (TinyML) technology, and an IoT system is developed with small size and low energy consumption. The system uses a Micro-Electro-Mechanical System (MEMS) sensor to collect acceleration data for machine learning training. A neural network model for recognizing the running state of rail vehicles is built and trained by defining a machine learning running state classification model. The trained recognition model is deployed to the IoT edge device at the vehicle side, and an offset time window method is utilized for real-time state sensing. In addition, the sensing results are uploaded to the IoT server for visualization. The experiments on the subway vehicle showed that the system could identify six complex running states in real-time with over 99% accuracy using only one IoT microcontroller. The model with three axes converges faster than the model with one. The model recognition accuracy remained above 98% and 95%, under different installation positions on the rail vehicle and the zero-drift phenomenon of the MEMS acceleration sensor, respectively. The presented method and system can also be extended to edge-aware applications of equipment such as automobiles and ships.

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Top 10 Published Articles of IEEE Access

Over the past ten years, IEEE Access has published some of the most groundbreaking research in electrical and electronics engineering and computer science. In celebration of the 10 Year Publishing Anniversary, our Editors have selected the Top 10 articles published in IEEE Access over the last decade based on downloads, citations, and overall impact in IEEE fields of interest.

Millimeter Wave Mobile Communications for 5G Cellular: It Will Work! (Published in 2013)

Authors: Theodore S. Rappaport, Shu Sun, Rimma Mayzus, Hang Zhao, Yaniv Azar, Kevin Wang, George N. Wong, Jocelyn K. Schulz, Mathew Samimi, Felix Gutierrez

Abstract: The global bandwidth shortage facing wireless carriers has motivated the exploration of the underutilized millimeter wave (mm-wave) frequency spectrum for future broadband cellular communication networks. There is, however, little knowledge about cellular mm-wave propagation in densely populated indoor and outdoor environments. Obtaining this information is vital for the design and operation of future fifth generation cellular networks that use the mm-wave spectrum. In this paper, we present the motivation for new mm-wave cellular systems, methodology, and hardware for measurements and offer a variety of measurement results that show 28 and 38 GHz frequencies can be used when employing steerable directional antennas at base stations and mobile devices.

3D Printing for the Rapid Prototyping of Structural Electronics (Published in 2014)

Authors: Eric Macdonald, Rudy Salas, David Espalin, Mireya Perez, Efrain Aguilera, Dan Muse, Ryan B. Wicker

Abstract: In new product development, time to market (TTM) is critical for the success and profitability of next generation products. When these products include sophisticated electronics encased in 3D packaging with complex geometries and intricate detail, TTM can be compromised – resulting in lost opportunity. The use of advanced 3D printing technology enhanced with component placement and electrical interconnect deposition can provide electronic prototypes that now can be rapidly fabricated in comparable time frames as traditional 2D bread-boarded prototypes; however, these 3D prototypes include the advantage of being embedded within more appropriate shapes in order to authentically prototype products earlier in the development cycle. The fabrication freedom offered by 3D printing techniques, such as stereolithography and fused deposition modeling have recently been explored in the context of 3D electronics integration – referred to as 3D structural electronics or 3D printed electronics. Enhanced 3D printing may eventually be employed to manufacture end-use parts and thus offer unit-level customization with local manufacturing; however, until the materials and dimensional accuracies improve (an eventuality), 3D printing technologies can be employed to reduce development times by providing advanced geometrically appropriate electronic prototypes. This paper describes the development process used to design a novelty six-sided gaming die. The die includes a microprocessor and accelerometer, which together detect motion and upon halting, identify the top surface through gravity and illuminate light-emitting diodes for a striking effect. By applying 3D printing of structural electronics to expedite prototyping, the development cycle was reduced from weeks to hours.

C2PS: A Digital Twin Architecture Reference Model for the Cloud-Based Cyber-Physical Systems (Published in 2017)

Authors: Kazi Masudul Alam, Abdulmotaleb El Saddik

Abstract: Cyber-physical system (CPS) is a new trend in the Internet-of-Things related research works, where physical systems act as the sensors to collect real-world information and communicate them to the computation modules (i.e. cyber layer), which further analyze and notify the findings to the corresponding physical systems through a feedback loop. Contemporary researchers recommend integrating cloud technologies in the CPS cyber layer to ensure the scalability of storage, computation, and cross domain communication capabilities. Though there exist a few descriptive models of the cloud-based CPS architecture, it is important to analytically describe the key CPS properties: computation, control, and communication. In this paper, we present a digital twin architecture reference model for the cloud-based CPS, C2PS, where we analytically describe the key properties of the C2PS. The model helps in identifying various degrees of basic and hybrid computation-interaction modes in this paradigm. We have designed C2PS smart interaction controller using a Bayesian belief network, so that the system dynamically considers current contexts. The composition of fuzzy rule base with the Bayes network further enables the system with reconfiguration capability. We also describe analytically, how C2PS subsystem communications can generate even more complex system-of-systems. Later, we present a telematics-based prototype driving assistance application for the vehicular domain of C2PS, VCPS, to demonstrate the efficacy of the architecture reference model.

Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (Published in 2018)

Authors: Amina Adadi, Mohammed Berrada

Abstract: At the dawn of the fourth industrial revolution, we are witnessing a fast and widespread adoption of artificial intelligence (AI) in our daily life, which contributes to accelerating the shift towards a more algorithmic society. However, even with such unprecedented advancements, a key impediment to the use of AI-based systems is that they often lack transparency. Indeed, the black-box nature of these systems allows powerful predictions, but it cannot be directly explained. This issue has triggered a new debate on explainable AI (XAI). A research field holds substantial promise for improving trust and transparency of AI-based systems. It is recognized as the sine qua non for AI to continue making steady progress without disruption. This survey provides an entry point for interested researchers and practitioners to learn key aspects of the young and rapidly growing body of research related to XAI. Through the lens of the literature, we review the existing approaches regarding the topic, discuss trends surrounding its sphere, and present major research trajectories.

5G Evolution: A View on 5G Cellular Technology Beyond 3GPP Release 15 (Published in 2019)

Authors: Amitabha Ghosh, Andreas Maeder, Matthew Baker, Devaki Chandramouli

Abstract: The 5G System is being developed and enhanced to provide unparalleled connectivity to connect everyone and everything, everywhere. The first version of the 5G System, based on the Release 15 (“Rel-15”) version of the specifications developed by 3GPP, comprising the 5G Core (5GC) and 5G New Radio (NR) with 5G User Equipment (UE), is currently being deployed commercially throughout the world both at sub-6 GHz and at mmWave frequencies. Concurrently, the second phase of 5G is being standardized by 3GPP in the Release 16 (“Rel-16”) version of the specifications which will be completed by March 2020. While the main focus of Rel-15 was on enhanced mobile broadband services, the focus of Rel-16 is on new features for URLLC (Ultra-Reliable Low Latency Communication) and Industrial IoT, including Time Sensitive Communication (TSC), enhanced Location Services, and support for Non-Public Networks (NPNs). In addition, some crucial new features, such as NR on unlicensed bands (NR-U), Integrated Access & Backhaul (IAB) and NR Vehicle-to-X (V2X), are also being introduced as part of Rel-16, as well as enhancements for massive MIMO, wireless and wireline convergence, the Service Based Architecture (SBA) and Network Slicing. Finally, the number of use cases, types of connectivity and users, and applications running on top of 5G networks, are all expected to increase dramatically, thus motivating additional security features to counter security threats which are expected to increase in number, scale and variety. In this paper, we discuss the Rel-16 features and provide an outlook towards Rel-17 and beyond, covering both new features and enhancements of existing features. 5G Evolution will focus on three main areas: enhancements to features introduced in Rel-15 and Rel-16, features that are needed for operational enhancements, and new features to further expand the applicability of the 5G System to new markets and use cases.

Wireless Communications Through Reconfigurable Intelligent Surfaces (Published in 2019)

Authors: Ertugrul Basar, Marco Di Renzo, Julien De Rosny, Merouane Debbah, Mohamed-Slim Alouini, Rui Zhang

Abstract: The future of mobile communications looks exciting with the potential new use cases and challenging requirements of future 6th generation (6G) and beyond wireless networks. Since the beginning of the modern era of wireless communications, the propagation medium has been perceived as a randomly behaving entity between the transmitter and the receiver, which degrades the quality of the received signal due to the uncontrollable interactions of the transmitted radio waves with the surrounding objects. The recent advent of reconfigurable intelligent surfaces in wireless communications enables, on the other hand, network operators to control the scattering, reflection, and refraction characteristics of the radio waves, by overcoming the negative effects of natural wireless propagation. Recent results have revealed that reconfigurable intelligent surfaces can effectively control the wavefront, e.g., the phase, amplitude, frequency, and even polarization, of the impinging signals without the need of complex decoding, encoding, and radio frequency processing operations. Motivated by the potential of this emerging technology, the present article is aimed to provide the readers with a detailed overview and historical perspective on state-of-the-art solutions, and to elaborate on the fundamental differences with other technologies, the most important open research issues to tackle, and the reasons why the use of reconfigurable intelligent surfaces necessitates to rethink the communication-theoretic models currently employed in wireless networks. This article also explores theoretical performance limits of reconfigurable intelligent surface-assisted communication systems using mathematical techniques and elaborates on the potential use cases of intelligent surfaces in 6G and beyond wireless networks.

Beyond Herd Immunity Against Strategic Attackers (Published in 2020)

Authors: Vilc Queupe, Leandro Pfleger De Aguiar, Daniel Sadoc Menasche, Cabral Lima, Italo Cunha, Eitan Altman, Rachid El-Azouzi, Francesco De Pellegrini, Alberto Avritzer, Michael Grottke

Abstract: Herd immunity, one of the most fundamental concepts in network epidemics, occurs when a large fraction of the population of devices is immune against a virus or malware. The few individuals who have not taken countermeasures against the threat are assumed to have very low chances of infection, as they are indirectly protected by the rest of the devices in the network. Although very fundamental, herd immunity does not account for strategic attackers scanning the network for vulnerable nodes. In face of such attackers, nodes who linger vulnerable in the network become easy targets, compromising cybersecurity. In this paper, we propose an analytical model which allows us to capture the impact of countermeasures against attackers when both endogenous as well as exogenous infections coexist. Using the proposed model, we show that a diverse set of potential attacks produces non-trivial equilibria, some of which go counter to herd immunity; e.g., our model suggests that nodes should adopt countermeasures even when the remainder of the nodes has already decided to do so.

Unsupervised K-Means Clustering Algorithm (Published in 2020)

Authors: Kristina P. Sinaga, Miin-Shen Yang

Abstract: The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it is an unsupervised learning to clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by initializations with a necessary number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. In this paper, we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations without parameter selection and can also simultaneously find an optimal number of clusters. That is, we propose a novel unsupervised k-means (U-k-means) clustering algorithm with automatically finding an optimal number of clusters without giving any initialization and parameter selection. The computational complexity of the proposed U-k-means clustering algorithm is also analyzed. Comparisons between the proposed U-k-means and other existing methods are made. Experimental results and comparisons actually demonstrate these good aspects of the proposed U-k-means clustering algorithm.

Enabling Large Intelligent Surfaces With Compressive Sensing and Deep Learning (Published in 2021)

Authors: Abdelrahman Taha, Muhammad Alrabeiah, Ahmed Alkhateeb

Abstract: Employing large intelligent surfaces (LISs) is a promising solution for improving the coverage and rate of future wireless systems. These surfaces comprise massive numbers of nearly-passive elements that interact with the incident signals, for example by reflecting them, in a smart way that improves the wireless system performance. Prior work focused on the design of the LIS reflection matrices assuming full channel knowledge. Estimating these channels at the LIS, however, is a key challenging problem. With the massive number of LIS elements, channel estimation or reflection beam training will be associated with (i) huge training overhead if all the LIS elements are passive (not connected to a baseband) or with (ii) prohibitive hardware complexity and power consumption if all the elements are connected to the baseband through a fully-digital or hybrid analog/digital architecture. This paper proposes efficient solutions for these problems by leveraging tools from compressive sensing and deep learning. First, a novel LIS architecture based on sparse channel sensors is proposed. In this architecture, all the LIS elements are passive except for a few elements that are active (connected to the baseband). We then develop two solutions that design the LIS reflection matrices with negligible training overhead. In the first approach, we leverage compressive sensing tools to construct the channels at all the LIS elements from the channels seen only at the active elements. In the second approach, we develop a deep-learning based solution where the LIS learns how to interact with the incident signal given the channels at the active elements, which represent the state of the environment and transmitter/receiver locations. We show that the achievable rates of the proposed solutions approach the upper bound, which assumes perfect channel knowledge, with negligible training overhead and with only a few active elements, making them promising for future LIS systems.

DNN-Based Indoor Localization Under Limited Dataset Using GANs and Semi-Supervised Learning (Published in 2022)

Authors: Wafa Njima, Ahmad Bazzi, Marwa Chafii

Abstract: Indoor localization techniques based on supervised learning deliver great performance accuracy while maintaining low online complexity. However, such systems require massive amounts of data for offline training, which necessitates costly measurements. The essence of this paper is twofold with the purpose of providing solutions to missing data of different nature: available unlabeled data and missing unlabeled data. In both cases, we rely on a few labeled available data, which is costly yet insufficient to achieve a high localization accuracy. To address the problem of available unlabeled data, a weighted semi-supervised DNN-based indoor localization approach leveraging pseudo-labeling methods in combination with real labeled samples and inexpensive pseudo-labeled samples is proposed in order to boost localization accuracy, while overcoming the high cost of collecting additional labeled data. As for the extreme case of unavailable unlabeled data, we propose an alternative localization system generating fake fingerprints based on generative adversarial networks (GANs) named ’Weighted GAN based indoor localization’. Furthermore, a deep neural network is trained on a mixed dataset containing both real collected and fake produced data samples using a similar weighting technique in order to improve location prediction performance and avoids overfitting. In terms of localization accuracy, our proposed localization approaches outperform conventional supervised localization schemes utilizing the same collection of real labeled samples. We have tested our proposed methods on both simulated data and experimental data from the publicly available UJIIndoorLoc database, which is built to test indoor positioning systems relying on Wi-Fi fingerprints. Results based on experimental data provide the localization accuracy increase compared to the classical supervised learning method using the same set of labeled collected data when using the weighted semi-supervised and the weighted-GAN approaches by $10.11~\%$ and $8.53~\%$ , respectively.

IEEE Access 10 Year Anniversary Twitter Giveaway

In celebration of the 10 Year Publishing Anniversary of IEEE Access, the journal will be hosting a Twitter Giveaway to thank our authors for choosing IEEE Access to publish their important research over the last decade.  For the entire month of May, share a link on Twitter to your published IEEE Access article on IEEE Xplore for a chance to win an IEEE Access swag bag!  

Be sure to use the hashtag #10YearsofIEEEAccess and tag us @IEEEAccess to qualify. Ten (10) winners will be randomly selected on June 1st, and will receive  a “swag bag” containing IEEE Access branded items such as a padfolio, a metal tumbler, a spiral notebook, pens, cloth tote bag, and keychain flashlights (valued at approximately $100).

For more information please see the Rules below.

To view the Top 10 Published IEEE Access articles since inception, please click here.

Official Rules

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Design, Modeling, and Analysis of a 3-D Spiral Inductor With Magnetic Thin-Films for PwrSoC/PwrSiP DC-DC Converters

A solution architecture for monolithic system-on-chip (SoC) power conversion is in high demand to enable modern electronics with a reduced footprint and increased functionality. A promising solution is to reduce the microinductor size by using novel magnetically-enhanced 3-D design topologies. This work presents the design, modeling, and analysis of a 3-D spiral inductor with magnetic thin-films for power supply applications in the frequency range of 3–30 MHz. A closed-form analytical expression is derived for the inductance, including both the air- and magnetic-core contributions. To validate the air-core inductance model, we implement a 3-D spiral inductor on PCB. The theoretical calculation of air-core inductance is in good agreement with experimental data. To validate the inductance model of the magnetic-core, a 3-D spiral inductor is modeled with Ansys Maxwell electromagnetic field simulation software. A winding AC resistance model is additionally presented. We perform a design space exploration (DSE) to investigate the significance of the 3-D spiral inductor structure. Two important performance parameters are discussed: dc quality factor (Qdc) and ac quality factor (Qac) . Also, a 3-D spiral inductor structure with magnetic thin-films is characterized in Ansys Maxwell to estimate its potential, and a novel fabrication method is proposed to implement this inductor. The measured relative permeability ( μr ) and the magnetic loss tangent ( tan δ ) of Co-Zr-Ta-B magnetic thin-films, developed in-house, are used to simulate the proposed structure. The promising results of the DSE can be easily extended to improve the performance of other 3-D inductor topologies, such as the solenoid and the toroid. The numerical simulations reveal that the 3-D spiral inductor with magnetic thin-films has the potential to demonstrate a figure-of-merit (FOM) that is significantly higher than traditional inductors.

Published in the IEEE Magnetics Society Section of IEEE Access.

View this article on IEEE Xplore

Single-Longitudinal-Mode Thulium-Doped Fiber Laser With Sub-kHz Linewidth Based on a Triple-Coupler Double-Ring Cavity

We propose and demonstrate a stable single-longitudinal mode (SLM) thulium-doped fiber laser (TDFL) with a Fabry- Pérot (F-P) fiber Bragg grating (FBG) filter and a triple-coupler based double-ring cavity (TC-DRC) filter. For the first time, this structure of TC-DRC filter is used to select a single mode from dense longitudinal-modes in a ring cavity TDFL. The design and fabricate methods of TC-DRC filter are revealed and the principle of SLM selection is also analyzed in detail. The experimental results demonstrated the good performance of the proposed filter. The central wavelength of proposed TDFL is 2049.49 nm and its OSNR is higher than 35 dB. There is no obvious wavelength drift during the test, and the power fluctuation is less 0.5 dB. The SLM operation is verified through the self-homodyne method, this laser can be stably maintained in a SLM state after operating for one hour under laboratory condition. In addition, the linewidth is measured less than 10 kHz based on the phase noise demodulation method.

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An Intraday Market Design for Colombia’s Energy Transition

The massive promotion of intermittent renewable resources as a decarbonization strategy for economies has led to the need to reconsider current market designs in order to facilitate the integration of this kind of production technology. The intraday market has established itself as an efficient mechanism for these purposes, as has been seen in diverse international experiences, principally Europe. The Colombian electricity market is no stranger to the needs of these reforms, as the National Energy and Mining Planning Office of Colombia (UPME in Spanish) estimates that by 2034 almost 30% of the electricity generation matrix will be comprised of intermittent renewable resources. This paper develops several elements to be considered for the implementation of an intraday market in Colombia, backed with quantitative information, contrary to previous studies that have based their recommendations on qualitative elements. The number of discrete intraday sessions, the effect of changing from a sequential energy and reserve allocation scheme to a co-optimized one, as well as the adjustment mechanism – or a balance mechanism – after the gate closure are, among other aspects, analyzed in this study. The computational simulations executed with real data from two operating months with different characteristics in terms of prices, unavailable assets – a liquidity factor for the new market – and system contingencies support the design elements developed in this study. That is, the proposed design – four sections, co-optimization of energy and reserve, and an adjustment mechanism that is not based only on the use of reserves – tested with real data confirm that this proposal is more convenient than the other kind of design from an operating cost and unavailability management perspective. The results reveal market design elements that must be considered in Colombia and serve as input for other countries – particularly Latin American countries – that are in the process of updating their electricity market designs as part of the current energy transition.

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Distributed Current Source Method for Modeling of Magnetic and Eddy-Current Fields in Sensing System Design

This paper presents a distributed current source (DCS) method for modeling the eddy current (EC) fields induced in biological or non-ferrous metallic objects in two-dimensional axisymmetric and three-dimensional Cartesian coordinates. The EC fields induced in the objects, magnetic flux density (MFD) in space, and magnetic flux (MF) of the sensing coils are formulated in state-space representation. The harmonic responses of the eddy current fields and electromotive force (EMF) of the sensing coil are formulated in closed-form solutions. The proposed DCS method is applied to design two eddy current sensing systems. The Bio-Differential Eddy Current (BD-EC) sensor distinguishes biological objects, and the Metal-Coaxial Eddy Current (MC-EC) sensor classifies non-ferrous metallic objects. The simulated EC field and EMF are numerically verified by comparing results with finite element analysis. An example is utilized to illustrate the advantage of the DCS method for calculating the MFD, MF, and EMF contributed from the induced ECD in the objects directly, and the EMF generated from each material. The proposed method, along with a prototype of the BD-EC sensor, has been experimentally evaluated on sweep frequency analysis for detecting meat and bone.

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Video Based Mobility Monitoring of Elderly People Using Deep Learning Models

In recent years, the number of older people living alone has increased rapidly. Innovative vision systems to remotely assess people’s mobility can help healthy, active, and happy aging. In the related literature, the mobility assessment of older people is not yet widespread in clinical practice. In addition, the poor availability of data typically forces the analyses to binary classification, e.g. normal/anomalous behavior, instead of processing exhaustive medical protocols. In this paper, real videos of elderly people performing three mobility tests of a clinical protocol are automatically categorized, emulating the complex evaluation process of expert physiotherapists. Videos acquired using low-cost cameras are initially processed to obtain skeletal information. A proper data augmentation technique is then used to enlarge the dataset variability. Thus, significant features are extracted to generate a set of inputs in the form of time series. Four deep neural network architectures with feedback connections, even aided by a preliminary convolutional layer, are proposed to label the input features in discrete classes or to estimate a continuous mobility score as the result of a regression task. The best results are achieved by the proposed Conv-BiLSTM classifier, which achieves the best accuracy, ranging between 88.12% and 90%. Further comparisons with shallow learning classifiers still prove the superiority of the deep Conv-BiLSTM classifier in assessing people’s mobility, since deep networks can evaluate the quality of test executions.

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Code Generation Using Machine Learning: A Systematic Review

Recently, machine learning (ML) methods have been used to create powerful language models for a broad range of natural language processing tasks. An important subset of this field is that of generating code of programming languages for automatic software development. This review provides a broad and detailed overview of studies for code generation using ML. We selected 37 publications indexed in arXiv and IEEE Xplore databases that train ML models on programming language data to generate code. The three paradigms of code generation we identified in these studies are description-to-code, code-to-description, and code-to-code. The most popular applications that work in these paradigms were found to be code generation from natural language descriptions, documentation generation, and automatic program repair, respectively. The most frequently used ML models in these studies include recurrent neural networks, transformers, and convolutional neural networks. Other neural network architectures, as well as non-neural techniques, were also observed. In this review, we have summarized the applications, models, datasets, results, limitations, and future work of 37 publications. Additionally, we include discussions on topics general to the literature reviewed. This includes comparing different model types, comparing tokenizers, the volume and quality of data used, and methods for evaluating synthesized code. Furthermore, we provide three suggestions for future work for code generation using ML.

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On the History and Future of 100% Renewable Energy Systems Research

Research on 100% renewable energy systems is a relatively recent phenomenon. It was initiated in the mid-1970s, catalyzed by skyrocketing oil prices. Since the mid-2000s, it has quickly evolved into a prominent research field encompassing an expansive and growing number of research groups and organizations across the world. The main conclusion of most of these studies is that 100% renewables is feasible worldwide at low cost. Advanced concepts and methods now enable the field to chart realistic as well as cost- or resource-optimized and efficient transition pathways to a future without the use of fossil fuels. Such proposed pathways in turn, have helped spur 100% renewable energy policy targets and actions, leading to more research. In most transition pathways, solar energy and wind power increasingly emerge as the central pillars of a sustainable energy system combined with energy efficiency measures. Cost-optimization modeling and greater resource availability tend to lead to higher solar photovoltaic shares, while emphasis on energy supply diversification tends to point to higher wind power contributions. Recent research has focused on the challenges and opportunities regarding grid congestion, energy storage, sector coupling, electrification of transport and industry implying power-to-X and hydrogen-to-X, and the inclusion of natural and technical carbon dioxide removal (CDR) approaches. The result is a holistic vision of the transition towards a net-negative greenhouse gas emissions economy that can limit global warming to 1.5°C with a clearly defined carbon budget in a sustainable and cost-effective manner based on 100% renewable energy-industry-CDR systems. Initially, the field encountered very strong skepticism. Therefore, this paper also includes a response to major critiques against 100% renewable energy systems, and also discusses the institutional inertia that hampers adoption by the International Energy Agency and the Intergovernmental Panel on Climate Change, as well as possible negative connections to community acceptance and energy justice. We conclude by discussing how this emergent research field can further progress to the benefit of society.

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