Nanoflowers Versus Magnetosomes: Comparison Between Two Promising Candidates for Magnetic Hyperthermia Therapy

Magnetic Fluid Hyperthermia mediated by iron oxide nanoparticles is one of the most promising therapies for cancer treatment. Among the different candidates, magnetite and maghemite nanoparticles have revealed to be some of the most promising candidates due to both their performance and their biocompatibility. Nonetheless, up to date, the literature comparing the heating efficiency of magnetite and maghemite nanoparticles of similar size is scarce. To fill this gap, here we provide a comparison between commercial Synomag Nanoflowers (pure maghemite) and bacterial magnetosomes (pure magnetite) synthesized by the magnetotactic bacterium Magnetospirillum gryphiswaldense of ⟨D⟩≈ 40 –45 nm. Both types of nanoparticles exhibit a high degree of crystallinity and an excellent degree of chemical purity and stability. The structural and magnetic properties in both nanoparticle ensembles have been studied by means of X–Ray Diffraction, Transmission Electron Microscopy, X–Ray Absorption Spectroscopy, and SQUID magnetometry. The heating efficiency has been analyzed in both systems using AC magnetometry at several field amplitudes (0–88 mT) and frequencies (130, 300, and 530 kHz).

View this article on IEEE Xplore

Published in the IEEE Magnetics Society Section.

Enter the Best Video Award for the Opportunity to Win $500

The 2024 IEEE Access Best Video Award Part 1 is a contest to win a prize of a $500 USD Amazon gift card for a corresponding author who submits the best video with an IEEE Access article submission. The best video will be selected by an independent editorial judging committee.

Once you submit your article and video through the IEEE Author Portal, and review and sign the electronic copy of the Terms and Conditions below, your video will be eligible to be considered for the award.

Currently accepting entries for articles with video submitted between January 1, 2024 and June 30, 2024.

*All submissions must be original and cannot include any third party content. Submissions that include third party content may be disqualified.

Helpful tips for creating a video.

To participate in this award, please complete the following form:

An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging

Underwater images play a key role in ocean exploration but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. First, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of the state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analyses (the used code is freely available at https://github.com/wangyanckxx/Single–Underwater–Image–Enhancement–and–Color–Restoration). Starting from this paper, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field.

View this article on IEEE Xplore

Phantom Malware: Conceal Malicious Actions From Malware Detection Techniques by Imitating User Activity

State of the art malware detection techniques only consider the interaction of programs with the operating system’s API (system calls) for malware classification. This paper demonstrates that techniques like these are insufficient. A point that is overlooked by the currently existing techniques is presented in this paper: Malware is able to interact with windows providing the corresponding functionality in order to execute the desired action by mimicking user activity. In other words, harmful actions will be masked as simulated user actions. To start with, the article introduces User Imitating techniques for concealing malicious commands of the malware as impersonated user activity. Thereafter, the concept of Phantom Malware will be presented: This malware is constantly applying User Imitating to execute each of its malicious actions. A Phantom Ransomware (ransomware employs the User Imitating for every of its malicious actions) is implemented in C++ for testing anti-virus programs in Windows 10. Software of various manufacturers are applied for testing purposes. All of them failed without exception. This paper analyzes the reasons why these products failed and further, presents measures that have been developed against Phantom Malware based on the test results.

View this article on IEEE Xplore

Security Hardening of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks

Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the security threats and mitigation for AI-powered applications in NextG networks have not been investigated deeply in academia and industry due to being new and more complicated. This paper focuses on an AI-powered IRS implementation in NextG networks along with its vulnerability against adversarial machine learning attacks. This paper also proposes the defensive distillation mitigation method to defend and improve the robustness of the AI-powered IRS model, i.e., reduce the vulnerability. The results indicate that the defensive distillation mitigation method can significantly improve the robustness of AI-powered models and their performance under an adversarial attack.

View this article on IEEE Xplore

Rotation Representations and Their Conversions

A rigid body motion, which can be decomposed into rotation and translation, is essential for engineers and scientists who deal with moving systems in a space. While translation is as simple as vector addition, rotation is hard to understand because rotations are non-Euclidean, and there are many ways to represent them. Additionally, each representation comes with complex operations, and the conversions between different representations are not unique. Therefore, in this tutorial we review rotation representations which are widely used in industry and academia such as rotation matrices, Euler angles, rotation axis-angles, unit complex numbers, and unit quaternions. In particular, for better understanding we begin with rotations in a two dimensional space and extend them to a three dimensional space. In that context, we learn how to represent rotations in a two dimensional space with rotation angles and unit complex numbers, and extend them respectively to Euler angles and unit quaternions for rotations in a three dimensional space. The definitions and properties of mathematical entities used for representing rotations as well as the conversions between various rotation representations are summarized in tables for the reader’s later convenience.

View this article on IEEE Xplore

Tool Wear Monitoring Based on Transfer Learning and Improved Deep Residual Network

Considering the complex structure weight of the existing tool wear state monitoring model based on deep learning, prone to over-fitting and requiring a large amount of training data, a monitoring method based on Transfer Learning and Improved Deep Residual Network is proposed. First, the data is preprocessed, one-dimensional cutting force data are transformed into two-dimensional spectrum by wavelet transform. Then, the Improved Deep Residual Network is built and the residual module structure is optimized. The Dropout layer is introduced and the global average pooling technique is used instead of the fully connected layer. Finally, the Improved Deep Residual Network is used as the pre-training network model and the tool wear state monitoring model combined with the model-based Transfer Learning method is constructed. The results show that the accuracy of the proposed monitoring method is up to 99.74%. The presented network model has the advantages of simple structure, small number of parameters, good robustness and reliability. The ideal classification effect can be achieved with fewer iterations.

View this article on IEEE Xplore

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.

View this article on IEEE Xplore

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

NO PURCHASE OR PAYMENT IS NECESSARY TO ENTER OR WIN. A PURCHASE WILL NOT IMPROVE YOUR ODDS OF WINNING. 

Contest: IEEE Access 10 Year Anniversary Twitter Giveaway (the “Contest”) 

Sponsor: The Institute of Electrical and Electronics Engineers, Incorporated, 445 Hoes Lane, Piscataway, New Jersey, USA, 08854 (“Sponsor”) 

Eligibility: Contest is open to residents of the United States of America and other countries, where permitted by local law, who are the age of eighteen (18) and older. Employees of Sponsor, its agents, affiliates and their immediate families are not eligible to enter Contest. Entrants may be subject to rules imposed by their institution or employer relative to their participation in contests and should check with their institution or employer for any relevant policies. Void where prohibited by law. 

Agreement to Official Rules: By participating in this contest, entrants agree to abide by the terms and conditions thereof as established by Sponsor. Sponsor reserves the right to alter any of these Official Rules at any time and for any reason. All decisions made by Sponsor concerning the Contest including, but not limited to the cancellation of the Contest, shall be final and at its sole discretion. 

Entry Period: This Contest commences from Monday, 01 May 2023 12:00AM EDT to Wednesday, 31 May 2023 12:00AM EDT (“Entry Period”). Sponsor’s server is the official clock for the Contest. Entries received before or after the Entry Period are void. 

How To Enter: 

• Share your own published IEEE Access article on Twitter using the hashtag #10YearsofIEEEAccess and tagging @IEEEAccess

○ The Twitter post should include a link to your published IEEE Access article on IEEE Xplore. Links to other sites such as preprint servers or institutional websites do not qualify.

○ The Twitter post must include the hashtag #10YearsofIEEEAccess.

○ The Twitter post must tag the IEEE Access Twitter account @IEEEAccess. 

○ You must read and agree to these Official Rules

○ You only qualify for sharing an article that you are an author on.

○ All authors listed on a published IEEE Access article are permitted to share the same article on their respective Twitter accounts.  However, a published article can only win once and only 1 author can win.  The author of the selected Tweet was selected will be the winner.

○ For an example of a qualifying Twitter post, please visit our Contest website at https://ieeeaccess.ieee.org/about-ieee-access/announcements/10years_twittergiveaway/.

LIMIT THREE (3) ENTRIES PER PERSON. 

The participant is permitted to share three (3) of their published IEEE Access articles to participate.  The participant can only share a published article once but may share three (3) different published IEEE Access articles total.  An author can only win once.

Only entries submitted in accordance with these Official Rules will be eligible for consideration. No alternate means of entry permitted. All entries become the exclusive property of Sponsor and will not be acknowledged or returned. 

Selection of Winner: 

In celebration of ten (10) years of publishing, ten (10) winners will be selected randomly by random.org.  Only contestants that follow the criteria for inclusion will be able to win. 

Prize: The prize(s) for the Contest are being sponsored by IEEE. The winner(s) (the “Prize Winner(s)”) shall receive an IEEE Access “swag bag” containing 2 stylus pens, a cloth tote bag, a spiral notebook, a padfolio, 2 keychain flashlights, and a water tumbler, valued at approximately $100 (the “Prize”).  Only the contestant that shared the winning Twitter post will receive the Prize (as long as the person is an author on the shared IEEE Access article).

In the event that a winner is residing in a country that presents difficulty for shipping the Prize (i.e., the cost of shipping outweighs the cost of the Prize, embargo issues, etc.), the winner will receive an Amazon e-gift card of approximate equal value to the Prize (in USD).  The Amazon e-gift card cannot be requested, as this is at the discretion of the IEEE.

No cash in lieu of prize or substitution of prize permitted, except that Sponsor reserves the right to substitute a prize or prize component of equal or greater value in its sole discretion for any reason at time of award. Sponsor shall not be responsible for service obligations or warranty (if any) in relation to the prize(s). Prize may not be transferred prior to award. All other expenses associated with use of the prize, including, but not limited to local, state, or federal taxes on the Prize, are the sole responsibility of the winner. Winner(s) understand that delivery of a prize may be void where prohibited by law and agrees that Sponsor shall have no obligation to substitute an alternate prize when so prohibited. Amazon is not a sponsor or affiliated with this Contest.

The winner must provide proof of authorship.

Notification of Potential Prize Winners: The Prize Winners will be notified by e-mail within 14 days of the selection date. If a Prize Winner does not acknowledge acceptance of the Prize within 14 business days after being notified that he or she is a Prize Winner, or if a potential Prize Winner does not return the signed claim forms within the required time, or is otherwise found to be ineligible or not in full compliance with these Official Rules, another Prize Winner will be chosen using the same procedure specified above. Potential Prize Winner may be asked to execute and return to Sponsor a declaration of eligibility and liability/publicity release, where lawful, within fourteen (14) days of attempted notification. Winner may waive the right to receive the prize. The decisions of Sponsor are final and binding in all respects. 

Release, Publicity, and Privacy: By receipt of the Prize and/or, if requested, by signing an affidavit of eligibility and liability/publicity release, the Prize Winner consents to the use of his or her name, likeness, business name and address by Sponsor for advertising and promotional purposes, including but not limited to on Sponsor’s social media pages, without any additional compensation, except where prohibited. No entries will be returned. All entries become the property of Sponsor. The Prize Winner agrees to release and hold harmless Sponsor and its officers, directors, employees, affiliated companies, agents, successors and assigns from and against any claim or cause of action arising out of participation in the Contest. 

Sponsor assumes no responsibility for computer system, hardware, software or program malfunctions or other errors, failures, delayed computer transactions or network connections that are human or technical in nature, or for damaged, lost, late, illegible or misdirected entries; technical, hardware, software, electronic or telephone failures of any kind; lost or unavailable network connections; fraudulent, incomplete, garbled or delayed computer transmissions whether caused by Sponsor, the users, or by any of the equipment or programming associated with or utilized in this Contest; or by any technical or human error that may occur in the processing of submissions or downloading, that may limit, delay or prevent an entrant’s ability to participate in the Contest. 

Sponsor reserves the right, in its sole discretion, to cancel or suspend this contest and award a prize from entries received up to the time of termination or suspension should virus, bugs or other causes beyond Sponsor’s control, unauthorized human intervention, malfunction, computer problems, phone line or network hardware or software malfunction, which, in the sole opinion of Sponsor, corrupt, compromise or materially affect the administration, fairness, security or proper play of the contest or proper submission of entries. Sponsor is not liable for any loss, injury or damage caused, whether directly or indirectly, in whole or in part, from downloading data or otherwise participating in this Contest. 

Entrant acknowledges and agrees that Sponsor may collect, store, share, and otherwise use personally identifiable information provided during the registration and application process and the Contest, including, but not limited to, name, mailing address, phone number, and email address. Sponsor will use this information with accordance with its Privacy Policy (https://www.ieee.org/security_privacy.html), including for administering the Contest and verifying Entrant’s identity, postal address, and telephone number in the event an entry qualifies for a prize. Entrant’s information may also be transferred to countries outside the country of Entrant’s residence, including the United States. Such other countries may not have privacy laws

and regulations similar to those of the country of Entrant’s residence. If an Entrant does not provide the mandatory data required at registration, Sponsor reserves the right to disqualify the entry. 

Right to Use Entries: By entering the Contest, entrants grant Sponsor a non-exclusive, irrevocable, royalty-free, perpetual, worldwide right and license to reproduce, publish, display, edit and otherwise use the submitted Entries, and entrant’s full name and city and state/province/country of residence, photograph, likeness, voice and institutional affiliation, in print or any offline or online and other media for purposes of editorials, exhibition, advertising, publicity and promotion without additional compensation or permission, unless prohibited by law. 

Representations and Warranties Regarding Entries: By submitting an Entry, you represent and warrant that your Entry does not and shall not comprise, contain, or describe, as determined in Sponsor’s sole discretion: (A) false statements or any misrepresentations of your affiliation with a person or entity; (B) personally identifying information about you or any other person; (C) statements or other content that is false, deceptive, misleading, scandalous, indecent, obscene, unlawful, defamatory, libelous, fraudulent, tortious, threatening, harassing, hateful, degrading, intimidating, or racially or ethnically offensive; (D) conduct that could be considered a criminal offense, could give rise to criminal or civil liability, or could violate any law; (E) any advertising, promotion or other solicitation, or any third party brand name or trademark; or (F) any virus, worm, Trojan horse, or other harmful code or component. By submitting an Entry, you represent and warrant that you own the full rights to the Entry and have obtained any and all necessary consents, permissions, approvals and licenses to submit the Entry and comply with all of these Official Rules, and that the submitted Entry is your sole original work, has not been previously published, released or distributed, and does not infringe any third-party rights or violate any laws or regulations. 

Limitations of Liability/Reserved Rights: Neither Sponsor, its parent, subsidiary or affiliated companies, nor its advertising or promotional agencies shall have any obligation, liability or responsibility with regard to (i) entries that contain incorrect or inaccurate information or do not comply with these Official Rules, (ii) entries, prize claims or notifications that are lost, late, incomplete, illegible, unintelligible, damaged or otherwise not received by the intended recipient, in whole or in part, due to computer, technical or other error of any kind, (iii) telephone, electronic, hardware, software, network, Internet or computer malfunctions, failures or difficulties of any kind, (iv) any condition caused by events beyond the control of Sponsor that may cause the Contest to be disrupted or delayed, (v) any printing or typographical errors in these Official Rules or any other materials associated with the Contest, or (vi) any damages or losses of any kind caused by any prize or resulting from participation in the Contest, accessing, uploading or downloading data in connection with the Contest, or acceptance, possession or use of any prize. Sponsor, in its sole discretion, reserves the right to disqualify any entrant tampering with or abusing the entry process or the operation of the Contest or otherwise violating these Official Rules. Sponsor, in its sole discretion, further reserves the right to cancel, terminate, suspend or modify the Contest if the Contest cannot be completed as planned because of infection by computer virus, bugs, tampering, unauthorized intervention or technical failures of any sort and to select a winner from among eligible entries unaffected by such event, if any.

Disputes: EACH ENTRANT AGREES THAT: (1) ANY AND ALL DISPUTES, CLAIMS, AND CAUSES OF ACTION ARISING OUT OF OR IN CONNECTION WITH THIS CONTEST, OR ANY PRIZES AWARDED, SHALL BE RESOLVED INDIVIDUALLY, WITHOUT RESORTING TO ANY FORM OF CLASS ACTION, PURSUANT TO ARBITRATION CONDUCTED UNDER THE COMMERCIAL ARBITRATION RULES OF THE AMERICAN ARBITRATION ASSOCIATION THEN IN EFFECT, (2) ANY AND ALL CLAIMS, JUDGMENTS AND AWARDS SHALL BE LIMITED TO ACTUAL OUT-OF-POCKET COSTS INCURRED, INCLUDING COSTS ASSOCIATED WITH ENTERING THIS CONTEST, BUT IN NO EVENT ATTORNEYS’ FEES; AND (3) UNDER NO CIRCUMSTANCES WILL ANY ENTRANT BE PERMITTED TO OBTAIN AWARDS FOR, AND ENTRANT HEREBY WAIVES ALL RIGHTS TO CLAIM, PUNITIVE, INCIDENTAL, AND CONSEQUENTIAL DAMAGES, AND ANY OTHER DAMAGES, OTHER THAN FOR ACTUAL OUT-OF-POCKET EXPENSES, AND ANY AND ALL RIGHTS TO HAVE DAMAGES MULTIPLIED OR OTHERWISE INCREASED. ALL ISSUES AND QUESTIONS CONCERNING THE CONSTRUCTION, VALIDITY, INTERPRETATION AND ENFORCEABILITY OF THESE OFFICIAL RULES, OR THE RIGHTS AND OBLIGATIONS OF ENTRANT AND SPONSOR IN CONNECTION WITH THE CONTEST, SHALL BE GOVERNED BY, AND CONSTRUED IN ACCORDANCE WITH, THE LAWS OF THE STATE OF NEW JERSEY, WITHOUT GIVING EFFECT TO ANY CHOICE OF LAW OR CONFLICT OF LAW, RULES OR PROVISIONS (WHETHER OF THE STATE OF NEW JERSEY OR ANY OTHER JURISDICTION) THAT WOULD CAUSE THE APPLICATION OF THE LAWS OF ANY JURISDICTION OTHER THAN THE STATE OF NEW JERSEY. SPONSOR IS NOT RESPONSIBLE FOR ANY TYPOGRAPHICAL OR OTHER ERROR IN THE PRINTING OF THE OFFER OR ADMINISTRATION OF THE CONTEST OR IN THE ANNOUNCEMENT OF THE PRIZES. 

Contest Results and Official Rules: To obtain the identity of the prize winner and/or a copy of these Official Rules, send a self-addressed stamped envelope to Kimberly Rybczynski, IEEE, 445 Hoes Lane, Piscataway, NJ 08854-4141 USA.