IEEE International Symposium Research Articles

RFMiD: Retinal Image Analysis for multi-Disease Detection challenge

In the last decades, many publicly available large fundus image datasets have been collected for diabetic retinopathy, glaucoma, and age-related macular degeneration, and a few other frequent pathologies. These publicly available datasets were used to develop a computer-aided disease diagnosis system by training deep learning models to detect these frequent pathologies. One challenge limiting the adoption of a such system by the ophthalmologist is, computer-aided disease diagnosis system ignores sight-threatening rare pathologies such as central retinal artery occlusion or anterior ischemic optic neuropathy and others that ophthalmologists currently detect. Aiming to advance the state-of-the-art in automatic ocular disease classification of frequent diseases along with the rare pathologies, a grand challenge on “Retinal Image Analysis for multi-Disease Detection” was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI - 2021). This paper, reports the challenge organization, dataset, top-performing participants solutions, evaluation measures, and results based on a new “Retinal Fundus Multi-disease Image Dataset” (RFMiD). There were two principal sub-challenges: disease screening (i.e. presence versus absence of pathology — a binary classification problem) and disease/pathology classification (a 28-class multi-label classification problem). It received a positive response from the scientific community with 74 submissions by individuals/teams that effectively entered in this challenge. The top-performing methodologies utilized a blend of data-preprocessing, data augmentation, pre-trained model, and model ensembling. This multi-disease (frequent and rare pathologies) detection will enable the development of generalizable models for screening the retina, unlike the previous efforts that focused on the detection of specific diseases.

Guest Editorial Special Issue on the 2024 IEEE International Symposium on Circuits and Systems

Guest Editorial Special Issue on the 2024 IEEE International Symposium on Circuits and Systems

IEEE International Symposium on Phased Array Systems and Technology

IEEE International Symposium on Phased Array Systems and Technology

Guest Editorial—Selected Papers From the 2023 IEEE International Symposium on Circuits and Systems

Guest Editorial—Selected Papers From the 2023 IEEE International Symposium on Circuits and Systems

The future of tire energy: a novel one-end cap structure for sustainable energy harvesting

Piezoelectric energy harvesting is gaining popularity as an eco-friendly solution to harvest energy from tire deformation for tire condition monitoring systems in vehicles. Traditional piezoelectric harvesters, such as cymbal and bridge structures, cannot be used inside tires due to their design limitations. The wider adoption of renewable energy sources into the energy system is increasing rapidly, reflecting a global attraction toward the utilization of sustainable power sources (Aljendy et al. in Int J Power Energy Convers 12(4): 314–337, 2021; Yesner et al. in Evaluation of a novel piezoelectric bridge transducer. In: 2017 Joint IEEE International Symposium on the Applications of Ferroelectric (ISAF)/International Workshop on Acoustic Transduction Materials and Devices (IWATMD)/Piezoresponse Force Microscopy (PFM). IEEE, 2017). The growing interest in capturing energy from tire deformation for Tire Pressure Monitoring Systems (TPMS) aligns with this trend, providing a promising and self-sustaining alternative to traditional battery-powered systems. This study presents a novel one-end cap tire strain piezoelectric energy harvester (TSPEH) that can be used efficiently and reliably inside a tire. The interaction between the tire and energy harvester was analyzed using a decoupled modeling approach, which showed that stress concentration occurred along the edge of the end cap. The TSPEH generated a maximum voltage of 768 V under 2 MPa of load, resulting in an energy output of 32.645 J/rev under 1 MPa. The computational findings of this study were consistent with previous experimental investigations, confirming the reliability of the numerical simulations. The results suggest that the one-end cap structure can be an effective energy harvester inside vehicle tires, providing a valuable solution for utilizing one-end cap structures in high-deformation environments such as vehicle tires.

2024 IEEE International Symposium on EMC+SIPI: Keynote Speaker and Ask the Experts Panels Announced

2024 IEEE International Symposium on EMC+SIPI: Keynote Speaker and Ask the Experts Panels Announced

Announcing the Young Professional Activities at the 2024 IEEE International Symposium on EMC+SIPI in Phoenix, Arizona

Announcing the Young Professional Activities at the 2024 IEEE International Symposium on EMC+SIPI in Phoenix, Arizona

Notice of Violation of IEEE Publication Principles: An Efficient Nonlinear Quantized Constant Envelope Precoding for Massive MU-MIMO Systems

Notice of Violation of IEEE Publication Principles <BR><BR>"An Efficient Nonlinear Quantized Constant Envelope Precoding for Massive MU-MIMO Systems," <BR>by R. Liang, H. Li, W. Zhang, C. Liu and Y. Guo, <BR>in IEEE Systems Journal, Early Access <BR><BR>After a careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles. <BR><BR>This paper contains content from the papers cited below. Credit notices were used, but due to the absence of quotation marks or offset text, the copied content is not clearly referenced or specifically identified. <BR><BR>"VLSI Design of a 3-bit Constant-Modulus Precoder for Massive MU-MIMO," <BR>by O. Castañeda, S. Jacobsson, G. Durisi, T. Goldstein and C. Studer, <BR>in the Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS), May 2018 <BR><BR>"Quantized Precoding for Massive MU-MIMO," <BR>by S. Jacobsson, G. Durisi, M. Coldrey, T. Goldstein and C. Studer, <BR>in IEEE Transactions on Communications, vol. 65, no. 11, pp. 4670-4684, November 2017 <BR><BR> <br/> Massive multiuser multiple-input&#x2013;multiple-output (MU-MIMO) systems are foreseen as the key technology in next-generation wireless communication systems. However, many radio-frequency (RF) chains will require high power consumption and lots of hardware cost. One of the practical solutions is using low-resolution discrete phase shifters (PSs) for each antenna and RF chain. This article designs a nonlinear quantized constant envelope precoding algorithm for MU-MIMO systems with low-resolution discrete PS at the base station. In this algorithm, the quantized constant envelope precoding problem is presented as a nonconvex optimization problem. The multiuser interference is minimized by solving the optimal precoded vector and the associated precoding factors. First, we consider relaxing the nonconvex constraint on the 1-bit quantized constant envelope precoding to a continuous set of convex constraints on the unit circle. Afterwards, the semidefinite relaxation method is used to tackle the equivalent 1-bit quantized precoding problem. We further extend the algorithm to provide multi-bit quantized constant envelop precoding. Finally, the simulations are carried out under various conditions and compared with the most advanced precoding methods. The results demonstrate that this algorithm achieves a comparable performance to the existing precoding methods. At the same time, the corresponding performance-complexity tradeoff is more conducive to the expansion of the proposed algorithm to the multibit case.

(Invited) The Laser-Integrated FIB-SEM for Faster 3D Packages Characterization and Failure Analysis

The semiconductor industry is constantly looking for new technologies and materials to provide more functionality and performance in a smaller volume.Modern innovations in the development of 3D advance packaging require new methods of materials research and failure analysis. Three-dimensional multi-level electronic components, whose packages contain a stack of chips and interconnections, require visualization and analysis of undersurface structures withhigh resolution and performance [2].Modern methods of electron microscopy based on ion beam (FIB-SEM) sample preparation provide a high quality of the cross-sectional surface and accurateinformation about the subsurface structure, however, FIB-SEM does not provide access to deep buried structures and high speed of sample preparation, what is required by the modern electronics industry. A unique solution for cross-sectional access to deeply buried structures is the integration of a laser beam into the FIB-SEM (laser FIB), which provides fast removal of large volumes of material with high speed. Laser FIB provides all advantages of integrates a fs-laser for speed, a Ga+ beam for accuracy, and a SEM for high resolution imaging to enable the fastest workflows. The isolated laser chamber prevents contamination of the electron column and detectors, while ensuring sample integrity with automated transfer between the SEM and laser chamberunder vacuum. Integration of the laser and FIB into a single system, automation of process parameters and laser shuttling and correlative workflows provide the fastest results and highest success rates. Fs-laser ablation is a thermal so the laser affected zone is minimal, and it is often possible to image cross sectional structure immediately after laser ablation, without FIB polishing [1].The latest developments, implemented in the laser-integrated FIB-SEM, such as an automated shuttle, NavCam integration, preset recipes, and the Cross-jet option for cover glass protection, improve the cross-sectioning process and ensure the automation, repeatability, and efficiency of the sample preparation. The combination of batch mode and other parameters of the laser milling process improves the high ablation rate and surface quality, which is especially effective for wide-gap semiconductor cross-sections. A combination of 3D X-ray microscopy (XRM) techniques and integrated laser scanning electron microscopes with a focused ion beam for sample preparation provides a workflow to improve precise targeting for specific analysis of buriedfeatures and correlation with additional equipment to provide subsurface analysis [3]. We describe a workflow using 3D XRM and Laser FIB for precise targeting and high throughput results and demonstrate the application examples. Correlated workflows accelerate results by connecting tools to quickly navigate between multiple length scales and imaging modes.[1] B.Tordoff, C. Hartfield, A. Holwell, S.Hiller, M. Kaestner, S. Kelly, J. Lee, S. Müller, F. Perez-Willard, T.Volkenandt, R. White, T. Rodgers; The LaserFIB: new application opportunities combining a highperformance FIB-SEM with femtosecond laser processing in an integrated second chamber; AppliedMicroscopy 50 (2020), 24.[2] C.Hartfield, W.Harris, A. Gu, M.Terada, V.Viswanathan, L. Jiao and T. Rodgers; Emerging Technologiesfor Advanced 3D Package Characterization to Enable the More-Than-Moore Era; ECS Transactions 109(2022), 15.[3] A. Gu, M. Terada, H. Stegmann, T. Rodgers, C. Fu, Y. Yang; From System to Package to Interconnect:An Artificial Intelligence Powered 3D X-ray Imaging Solution for Semiconductor Package StructuralAnalysis and Correlative Microscopic Failure Analysis; 2022 IEEE International Symposium on thePhysical and Failure Analysis of Integrated Circuits (IPFA) (2022)

2023 IEEE International Symposium on Mixed and Augmented Reality

2023 IEEE International Symposium on Mixed and Augmented Reality

Call for Papers: IEEE International Symposium on Phased Array Systems and Technology

Call for Papers: IEEE International Symposium on Phased Array Systems and Technology

Automated Cephalometric Landmark Detection Using Deep Reinforcement Learning.

Accurate cephalometric landmark detection leads to accurate analysis, diagnosis, and surgical planning. Many studies on automated landmark detection have been conducted, however reinforcement learning-based networks have not yet been applied. This is the first study to apply deep Q-network (DQN) and double deep Q-network (DDQN) to automated cephalometric landmark detection to the best of our knowledge. The performance of the DQN-based network for cephalometric landmark detection was evaluated using the IEEE International Symposium of Biomedical Imaging (ISBI) 2015 Challenge data set and compared with the previously proposed methods. Furthermore, the clinical applicability of DQN-based automated cephalometric landmark detection was confirmed by testing the DQN-based and DDQN-based network using 500-patient data collected in a clinic. The DQN-based network demonstrated that the average mean radius error of 19 landmarks was smaller than 2mm, that is, the clinically accepted level, without data augmentation and additional preprocessing. Our DQN-based and DDQN-based approaches tested with the 500-patient data set showed the average success detection rate of 67.33% and 66.04% accuracy within 2mm, respectively, indicating the feasibility and potential of clinical application.

IEEE International Symposium on Phased Array Systems and Technology

IEEE International Symposium on Phased Array Systems and Technology

Sources of performance variability in deep learning-based polyp detection

PurposeValidation metrics are a key prerequisite for the reliable tracking of scientific progress and for deciding on the potential clinical translation of methods. While recent initiatives aim to develop comprehensive theoretical frameworks for understanding metric-related pitfalls in image analysis problems, there is a lack of experimental evidence on the concrete effects of common and rare pitfalls on specific applications. We address this gap in the literature in the context of colon cancer screening.MethodsOur contribution is twofold. Firstly, we present the winning solution of the Endoscopy Computer Vision Challenge on colon cancer detection, conducted in conjunction with the IEEE International Symposium on Biomedical Imaging 2022. Secondly, we demonstrate the sensitivity of commonly used metrics to a range of hyperparameters as well as the consequences of poor metric choices.ResultsBased on comprehensive validation studies performed with patient data from six clinical centers, we found all commonly applied object detection metrics to be subject to high inter-center variability. Furthermore, our results clearly demonstrate that the adaptation of standard hyperparameters used in the computer vision community does not generally lead to the clinically most plausible results. Finally, we present localization criteria that correspond well to clinical relevance.ConclusionWe conclude from our study that (1) performance results in polyp detection are highly sensitive to various design choices, (2) common metric configurations do not reflect the clinical need and rely on suboptimal hyperparameters and (3) comparison of performance across datasets can be largely misleading. Our work could be a first step towards reconsidering common validation strategies in deep learning-based colonoscopy and beyond.

Guest Editorial Special Issue on the 2023 IEEE International Symposium on Circuits and Systems

This special issue of the IEEE Transactions On Circuits and Systems— PART II: EXPRESS BRIEFS (TCAS-II) continues the successful tradition of the co-publication initiative started few years ago by the IEEE Circuits and Systems Society (CASS) to publish a selection of the best papers accepted for presentation at the IEEE International Symposium on Circuits and Systems (ISCAS). This year ISCAS is held in Monterey, California, United States of America, on May 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> – May 25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> , and the process for this Special Issue was carried out as soon as the paper selection was done. As TCAS-II only publishes 5-page briefs, and hence both conference and journal versions of selected works would be largely overlapped, the papers included in this Issue will not appear in the Proceedings of the IEEE ISCAS. Also, similar to what is done by other IEEE Societies, IEEE CASS intends to shift the role of IEEE conferences towards more networking events as well as opportunities for discussions on ongoing research efforts.

IEEE ISMAR 2022 Report: Toward Better Mixed Realities.

On October 21, 2022, the 21st IEEE International Symposium on Mixed and Augmented Reality (ISMAR 2022) was successfully completed in Singapore. ISMAR is the leading international conference in the fields of augmented reality, mixed reality, and virtual reality. This was the first time that ISMAR was held in Southeast Asia and the first time in hybrid mode. ISMAR 2022 achieved a historically high number of papers and attendees, witnessing the steady growth of the community and the scientific contributions. In this article, we report the key outcomes, impressions, research trends, and lessons learned from the conference.

Guest Editorial—Selected Papers From the 2022 IEEE International Symposium on Circuits and Systems

The IEEE International Symposium on Circuits and Systems (ISCAS) is the flagship conference of the IEEE Circuits and Systems (CAS) Society and the world's premiere forum for researchers in the fields of theory, design and implementation of circuits and systems. The IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS (TBioCAS) highlights selected papers from ISCAS on topics related to biological and healthcare applications. This special section features four selected papers from ISCAS 2022, held in Austin, Texas, USA from May 28 to June 1, 2022.

Looking to the New Year With Optimism [Message From the President

The year 2022 has ended, and the year end is always a time of taking summary. In 2022 we returned to relative normality after more than two years of the pandemic. The first event was finally a hybrid and not just an online meeting: the IEEE International Symposium on Industrial Electronics in Anchorage, Alaska. Then, we held an entirely stationary 48th Annual Conference of the IEEE Industrial Electronics Society (IES) in Brussels, Belgium, with around 800 participants. I would like to emphasize that the pandemic has shown that there is also interest in fully online conferences, so we have organized the first IES Annual Online Conference, which will have a permanent place in our portfolio of conferences.

The Liver Tumor Segmentation Benchmark (LiTS).

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.

The 14th IEEE RFIT Symposium, in Busan, South Korea [Conference Reports

The 2022 IEEE International Symposium on Radio Frequency Integration Technology (RFIT 2022) was held in Busan, South Korea, 29–31 August 2022. RFIT is an interdisciplinary advanced forum dealing with microwave and high-frequency microelectronics technologies. It serves as an opportunity to build the RF-integration technology communities and present the latest developments in RF circuit design, technology, and system integration.