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IEEE Journal on Flexible Electronics 2024年第8期3卷 374-382页

作者： Abdelatty, Mohamed Y. Alhendi, Mohammed Obeidat, Abdullah S. Umar, Ashraf Enakerakpo, Emuobosan Al-Haidari, Riadh Poliks, Mark D. State University of New York at Binghamton Department of Systems Science and Industrial Engineering The Center for Advanced Microelectronics Manufacturing BinghamtonNY13902 United States State University of New York at Binghamton The Center for Advanced Microelectronics Manufacturing BinghamtonNY13902 United States

Meshing of conventional printed circuit board (PCB) grounds refers to a process in which certain ground planes appear as copper lattices;regular openings are placed at regular intervals. The need for ground meshing for rigid PCBs has become minor with the development of micro-etching approaches. However, for flexible hybrid electronics (FHEs), meshed grounds could offer some benefits, such as being unsusceptible to bending and conserving material and time. In this article, we study how meshed ground planes affect the radio frequency (RF) performance of straight microstrip lines. Simulations show meshed grounds with more than 50% filling produce good RF performance. When the filling percentage of the meshed grounds is less than 50%, ripples in the insertion loss start to appear. To confirm the simulation results, dispensing and aerosol jet printing (AJP) systems were used to fabricate silver ink microstrip lines on PET substrates with different meshed ground patterns. The experimental measurement confirmed the simulation results. Bend testing was carried out to investigate the impact of mesh grounds on the RF performance of the microstrip lines after bending. The results demonstrate that the less-filled meshed ground samples are less susceptible to bending. As an extension of the work, we developed an empirical model to modify the microstrip line's width to smoothen the insertion loss ripples while maintaining the bending superiority. To experimentally validate the model, predictions from this empirical model were used to fabricate and measure some samples. © 2024 IEEE.

关键词： Substrates

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Hardware Acceleration for Embedded Scripting Programming Language ‘mruby/c’

Computer Software

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Computer Software 2024年第2期41卷 60-79页

作者： Maeda, Hiroyuki Tanaka, Kazuaki Fukuoka Industrial Technology Center Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Japan

In this paper, we propose hardware acceleration to improve a performance of scripting programming languages for embedded developments. Scripting programming languages enable more efficient software developments and scripting programming languages are used in several system developments such as IoT developments. However, scripting programming languages are an interpreted language and the program is executed by a virtual machine, so it takes a long time to execute. Therefore, we implemented hardware acceleration for scripting programming language "mruby/c" targeting small embedded devices. The hardware acceleration supports bottleneck processing of a virtual machine of mruby/c, execution speeds are improved. This paper describes the configuration of the hardware acceleration for mruby/c and the performance evaluation. © 2024 Japan Society for Software science and Technology. All rights reserved.

关键词： Virtual machine

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Chattering-Free Fault-Tolerant Cluster Control and Fault Direction Identification for HIL UAV Swarm With Pre-Specified Performance

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 183-197页

作者： Pei-Ming Liu Xiang-Gui Guo Jian-Liang Wang Daniel Coutinho Lihua Xie the Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing the Shunde Graduate School of University of Science and Technology Beijing IEEE Autonomous Intelligent Systems Department Hangzhou Innovation Institute of Beihang University the Department of Automation and Systems Universidade Federal de Santa Catarina the School of Electrical and Electronic Engineering Nanyang Technological University

In this paper, the problem of pre-specified performance fault-tolerant cluster consensus control and fault direction identification is solved for the human-in-the-loop(HIL) swarm unmanned aerial vehicles(UAVs) in the presence of possible nonidentical and unknown direction faults(NUDFs) in the yaw *** control strategy begins with the design of a pre-specified performance event-triggered observer for each individual *** observers estimate the outputs of the human controlled UAVs, and simultaneously achieve the distributed design of actual control signals as well as cluster consensus of the observer *** is worth mentioning that these observers require neither the high-order derivatives of the human controlled UAVs' output nor a priori knowledge of the initial conditions. The fault-tolerant controller realizes the pre-specified performance output regulation through error transformation and the Nussbaum function. It should be pointed out that there are no chattering caused by the jump of the Nussbaum function when a reverse fault occurs. In addition, to provide a basis for further solving the problem of physical malfunctions, a fault direction identification algorithm is proposed to accurately identify whether a reverse fault has occurred. Simulation results verify the effectiveness of the proposed control and fault direction identification strategies when the reverse faults occur.

关键词： Chattering-free cluster consensus fault direction identification human-in-the-loop (HIL) nonidentical and unknown direction faults (NUDFs) pre-specified performance swarm unmanned aerial vehicles (UAVs)

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Multilevel Discrete Cosine Transform and CNN-Based Image Steganography 3

Multilevel Discrete Cosine Transform and CNN-Based Image Ste...

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3rd International Conference on Intelligent systems, Advanced Computing, and Communication, ISACC 2025

作者： Subburaj, Brindha Sai, Aditya Shantanu, Shantanu Won, Daehan Vellore Institute of Technology School of Computer Science and Engineering Chennai India Binghamton University Department of Systems Science and Industrial Engineering United States

ISBN: (纸本)9798331523893

This paper investigates the growing role of steganography in cybersecurity and presents a hybrid implementation called Multi-level Discrete Cosine Convolution (MDCC) that applies a Multi-level Discrete Cosine Transform (MDCT) and a Convolutional Neural Network (CNN). Steganography provides some advantages compared to cryptography, mainly its invisibility, but there are weaknesses in information capacity and resistance to attacks in traditional techniques. The paper suggests solving these difficulties with a new steganography method using deep learning. The proposed MDCC method provides an opportunity to encrypt large amounts of data in realistic images that are beyond human visual perception. The system is trained on the ImageNet dataset by using a loss function to balance the quality of the cover image and the information it conceals within it. The MDCC model proposed yields accurate results for all metrics since PSNR, SSIM, and bits per pixel come out to be 80.13dB, 0.9918, and 12.5 bpp, respectively. © 2025 IEEE.

关键词： Discrete cosine transforms

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Real-time object segmentation for laparoscopic cholecystectomy using YOLOv8

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Neural Computing and Applications 2024年第4期37卷 2697-2710页

作者： Tashtoush, Amr Wang, Yong Khasawneh, Mohammad T. Hader, Asma Shazeeb, Mohammed Salman Lindsay, Clifford Grant School of Systems Science and Industrial Engineering State University of New York at Binghamton BinghamtonNY United States School of Medicine Jordan University of Science and Technology Al-Ramtha Irbid Jordan Department of Radiology University of Massachusetts Chan Medical School WorcesterMA United States

Organ and tool detection and segmentation in real time during surgery have been significant challenges in the development of robotic surgery. Most existing detection methods are unsuitable for the surgical environment, where the lighting conditions, occlusions, and anatomical structures can vary significantly. This study presents an organ and surgical tool segmentation and detection algorithm using a manually annotated dataset based on YOLOv8 (You Only Look Once), a state-of-the-art object detection framework. The YOLOv8 deep learning neural network is trained to detect and segment organs and tools during laparoscopic cholecystectomy using a manually annotated dataset of frames taken from actual surgeries. After four experiments using combinations of small and extra-large model sizes and the original and a modified dataset, the resulting algorithm is evaluated and tested in real time on a new surgical video. The method shows it can provide real-time feedback to the surgeon by accurately locating and segmenting the target organs displayed in the surgical video. The method outperforms the baseline methods, with a "bounding box" mean average precision (mAP50) and precession (P) of (50.2%, 51.6%), (52.8%, 76.9%), (83.2%, 81.1%), and (86.3%, 85.7%) for the first, second, third, and fourth experiments, respectively, and a "masking segment" of mAP50 and precession of (50.5%, 51.8%), (54.3%, 76.1%), (82.6%, 80.4%), (86.0%, 85.4%) for the first, second, third, and fourth experiments, respectively. The best-performing model has a speed of around 13.1 ms per frame. This novel application could be a stepping stone in future work, such as developing an algorithm to display the results to the surgeon in a heads-up-display (HUD) to help navigate the scenes or even be implemented in robotic surgeries. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Laparoscopy

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Behavioral and Topological Heterogeneities in Network Versions of Schelling’s Segregation Model

arXiv

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arXiv 2024年

作者： Deter, Will Sayama, Hiroki Binghamton Center of Complex Systems Department of Systems Science and Industrial Engineering Binghamton University United States

Agent-based models of residential segregation have been of persistent interest to various research communities since their origin with James Sakoda [1] and popularization by Thomas Schelling [2]. Frequently, these models have sought to elucidate the extent to which the collective dynamics of individual preferences may cause segregation to emerge. This open question has sustained relevance in U.S. jurisprudence. Previous investigation of heterogeneity of behaviors (preferences) by Xie & Zhou [3] has shown reductions in segregation. Meanwhile, previous investigation of heterogeneity of social network topologies by Gandica, Gargiulo, and Carletti [4] has shown no significant impact to observed segregation levels. In the present study, we examined the effects of the concurrent presence of both behavioral and topological heterogeneities in network segregation models. Simulations were conducted using both Schelling’s and Xie & Zhou’s preference models on 2D lattices with varied levels of densification to create topological heterogeneities (i.e., clusters, hubs). Results show a richer variety of outcomes, including novel differences in resultant segregation levels and hub composition. Notably, with concurrent increased representations of heterogeneous preferences and heterogenous topologies, reduced levels of segregation emerge. Simultaneously, we observe a novel dynamic of segregation between tolerance levels as highly tolerant nodes take residence in dense areas and push intolerant nodes to sparse areas mimicking the urban-rural divide. © 2024, CC BY-NC-ND.

关键词： Network topology

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CommSense: A Wearable Sensing Computational Framework for Evaluating Patient-Clinician Interactions

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Proceedings of the ACM on Human-Computer Interaction 2024年第CSCW2期8卷 1-31页

作者： Wang, Zhiyuan Hassan, Nusayer Lebaron, Virginia Flickinger, Tabor E. Ling, David Edwards, James Wu, Congyu Boukhechba, Mehdi Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States School of Nursing University of Virginia CharlottesvilleVA United States School of Medicine University of Virginia CharlottesvilleVA United States Department of Systems Science and Industrial Engineering Binghamton University BinghamtonNY United States Johnson & Johnson Innovative Medicine TitusvilleNJ United States

Quality patient-provider communication is critical to improve clinical care and patient outcomes. While progress has been made with communication skills training for clinicians, significant gaps exist in how to best monitor, measure, and evaluate the implementation of communication skills in the actual clinical setting. Advancements in ubiquitous technology and natural language processing make it possible to realize more objective, real-time assessment of clinical interactions and in turn provide more timely feedback to clinicians about their communication effectiveness. In this paper, we propose CommSense, a computational sensing framework that combines smartwatch audio and transcripts with natural language processing methods to measure selected "best-practice" communication metrics captured by wearable devices in the context of palliative care interactions, including understanding, empathy, presence, emotion, and clarity. We conducted a pilot study involving N=40 clinician participants, to test the technical feasibility and acceptability of CommSense in a simulated clinical setting. Our findings demonstrate that CommSense effectively captures most communication metrics and is well-received by both practicing clinicians and student trainees. Our study also highlights the potential for digital technology to enhance communication skills training for healthcare providers and students, ultimately resulting in more equitable delivery of healthcare and accessible, lower cost tools for training with the potential to improve patient outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Hospital data processing

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The Effects of Temperature and Relative Humidity on Intermetallic Compound Layer Growth: Implications for Solder Joint Reliability

The Effects of Temperature and Relative Humidity on Intermet...

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2025 Pan Pacific Strategic Electronics Symposium, Pan Pacific 2025

作者： Almaetah, Yazan Santos, D.L. Patel, Darshil Scheid, John Huang, Yaoli Pantone, Michael Shindadkar, Snehal School of Systems Science and Industrial Engineering Watson Institute for Systems Excellence T.J. Watson College of Engineering and Applied Science Binghamton University - State University of New York United States Foxconn Industrial Internet - North America United States

ISBN: (纸本)9781944543358

The growth of the Intermetallic Compound (IMC) layer is essential for the mechanical reliability and performance of solder joints in electronic devices. This study investigates how elevated temperature and relative humidity (RH) together influence IMC layer development, focusing on the SAC305 solder alloy. While temperature's role has been well-studied, the specific effects of humidity on IMC growth are less understood, and this research aims to fill that gap. We conducted controlled experiments under three conditions: 85°C at 30% RH, 85°C at 85% RH, and 180°C at 60% RH, over aging periods from 100 to 1000 hours. Using optical microscopy, we measured the IMC layer's thickness, finding that both temperature and humidity independently and synergistically affect growth. Higher humidity levels notably enhanced atomic diffusion across the solder joint interface, with IMC thickness following a square-root trend over time, indicative of diffusion-driven mechanisms. From this data, we developed a predictive model incorporating key factors like temperature and humidity. Our results show that humidity significantly impacts IMC growth. For example, at 85°C, the IMC layer was about 55% thicker in high humidity (85% RH) as compared to low humidity (30% RH). At 180°C with 60% RH, growth was 130% greater than in low-humidity conditions, highlighting the compounded effects of temperature and humidity. These findings closely match predictions from our empirical model, validating its reliability. The study also emphasizes the importance of environmental control, as high humidity significantly reduces durability in time-to-failure predictions. By highlighting humidity's role in IMC layer growth, this research offers valuable insights into environmental factors affecting solder joint reliability. The predictive model serves as a practical tool for designing electronic assemblies that can withstand challenging environments, ultimately enhancing durability and performance. © 2025 SMTA.

关键词： Soldering alloys

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VizTrust: A Visual Analytics Tool for Capturing User Trust Dynamics in Human-AI Communication

VizTrust: A Visual Analytics Tool for Capturing User Trust D...

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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Wang, Xin Jesso, Stephanie Tulk Kojaku, Sadamori Neyens, David M. Kim, Min Sun School of Systems Science and Industrial Engineering Binghamton University VestalNY United States Department of Industrial Engineering Clemson University ClemsonSC United States School of Communication and Information University of Hawaii at Manoa HonoluluHI United States

ISBN: (纸本)9798400713958

Trust plays a fundamental role in shaping the willingness of users to engage and collaborate with artificial intelligence (AI) systems. Yet, measuring user trust remains challenging due to its complex and dynamic nature. While traditional survey methods provide trust levels for long conversations, they fail to capture its dynamic evolution during ongoing interactions. Here, we present VizTrust1, which addresses this challenge by introducing a real-time visual analytics tool that leverages a multi-agent collaboration system to capture and analyze user trust dynamics in human-agent communication. Built on established human-computer trust scales—competence, integrity, benevolence, and predictability—, VizTrust enables stakeholders to observe trust formation as it happens, identify patterns in trust development, and pinpoint specific interaction elements that influence trust. Our tool offers actionable insights into human-agent trust formation and evolution in real time through a dashboard, supporting the design of adaptive conversational agents that responds effectively to user trust signals. © 2025 Copyright held by the owner/author(s).

关键词： Natural language processing systems

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LARGE LANGUAGE MODELS FOR HEALTHCARE TEXT CLASSIFICATION: A SYSTEMATIC REVIEW

arXiv

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arXiv 2025年

作者： Sakai, Hajar Lam, Sarah S. School of Systems Science and Industrial Engineering State University of University at Binghamton BinghamtonNY United States

Large Language Models (LLMs) have fundamentally transformed approaches to Natural Language Processing (NLP) tasks across diverse domains. In healthcare, accurate and cost-efficient text classification is crucial, whether for clinical notes analysis, diagnosis coding, or any other task, and LLMs present promising potential. Text classification has always faced multiple challenges, including manual annotation for training, handling imbalanced data, and developing scalable approaches. With healthcare, additional challenges are added, particularly the critical need to preserve patients’ data privacy and the complexity of the medical terminology. Numerous studies have been conducted to leverage LLMs for automated healthcare text classification and contrast the results with existing machine learning-based methods where embedding, annotation, and training are traditionally required. Existing systematic reviews about LLMs either do not specialize in text classification or do not focus on the healthcare domain. This research synthesizes and critically evaluates the current evidence found in the literature regarding the use of LLMs for text classification in a healthcare setting. Major databases (e.g., Google Scholar, Scopus, PubMed, science Direct) and other resources were queried, which focused on the papers published between 2018 and 2024 within the framework of PRISMA guidelines, which resulted in 65 eligible research articles. These were categorized by text classification type (e.g., binary classification, multi-label classification), application (e.g., clinical decision support, public health and opinion analysis), methodology, type of healthcare text, and metrics used for evaluation and validation. This review reveals the existing gaps in the literature and suggests future research lines that can be investigated and explored. © 2025, CC BY-NC-ND.

关键词： Data privacy

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