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arXiv 2020年

作者： Singh, Rohit Sicker, Douglas Engineering & Public Policy Carnegie Mellon University Pittsburgh United States School of Computer Science Carnegie Mellon University Pittsburgh United States

The increasing demand for ultra-high throughput in ultra-dense networks might take a toll on 5G capacity. Moreover, with Internet-of-Things (IoT) and the growing use-cases for indoor killer-applications, it will be necessary to look beyond 5G technologies. One such promising technology is to move higher in the frequencies, such as the THz (300 GHz-10 THz) spectrum. THz has a massive number of greenfield-contiguous channels ranging from 10 GHz to 200 GHz (best case), which was not available in the traditional radio frequency (RF) or millimeter wave (mmWave) bands. Although THz has immense potential to cater to such demands, it comes with numerous challenges revolving around hardware, link budget, mobility, blockages, scheduling, and deployment. Terahertz access points (THz-APs) are sensitive to deployment and can critically impact a system's dynamics (i.e., coverage, throughput, and efficiency). In this paper, we present Strategies for High-frequency INdoor Environments or SHINE, which focuses on efficient AP deployment in the THz spectrum and draws motivation from approaches used indoor to improve lighting conditions. Due to THz's limited coverage area of a few meters, the number of THz-APs required to satisfy a densely populated room will be higher compared to today’s single router/box/AP model. This increased number of THz-APs will not only increase the operational costs, but also (in some cases) can make the system inefficient. Through SHINE, we explore the deployment-related challenges and propose strategies to mitigate the same. Copyright © 2020, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Improving spectrum efficiency in heterogeneous networks using granular identification 13th

Improving spectrum efficiency in heterogeneous networks usin...

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13th EAI International Conference on Cognitive Radio Oriented Wireless Networks, CROWNCOM 2018

作者： Singh, Rohit Sicker, Douglas Engineering and Public Policy Carnegie Mellon University Pittsburgh United States School of Computer Science Carnegie Mellon University Pittsburgh United States

ISBN: (纸本)9783030054892

Given the ever-increasing demand for wireless services and the pending explosion of the Internet of Things (IoT), demand for radio spectrum will only become more acute. Setting aside (but not ignoring) the need for additional allocations of spectrum, the existing spectrum needs to be used more efficiently so that it can meet the demand. Other than providing more spectrum there are other factors (like, transmit power, antenna angles, QoS, bandwidth, and others) that can be adjusted to cater to the demand and at the same time increase the spectrum efficiency. With heterogeneity and densification these factors are so varied it becomes necessary that we have some tool to monitor these factors so as to optimize our outcome. Here we propose a PHY layer granular identification that monitors the physical and logical parameters associated with a device/antenna. Through a simple optimization problem, we show how the proposed identification mechanism can further the cause of spectrum efficiency and ease coordination among devices in a heterogeneous network (HetNet) to assign resources more optimally. Compared to received signal strength (RSS) way of assigning resources the proposed approach shows a $$138\%$$ to $$220\%$$ increase (depending on the requested QoS) in spectrum efficiency. Ultimately, this research is aimed at assisting the regulators in addressing future spectrum related efficiency and enforcement issues. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Heterogeneous networks

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Web-based Application for Screening Energy Efficiency Investments: A MCDA Approach

Web-based Application for Screening Energy Efficiency Invest...

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International Conference on Information, Intelligence, Systems and Applications (IISA)

作者： Aikaterini Papapostolou Filippos Dimitrios Mexis Elissaios Sarmas Charikleia Karakosta John Psarras School of Electrical and Computer Engineering Decision Support Systems Laboratory Energy Policy Unit National Technical University of Athens Athens Greece

ISBN: (数字)9781665422284

ISBN: (纸本)9781665422291

Energy Efficiency (EE) has been identified as one of the most cost-effective means aiming at reducing energy consumption while maintaining an equivalent level of economic activity. Mainstreaming EE financing is considered a key priority to avert climate change. The lack of evidence on the performance, commonly agreed procedures and standards for EE investments, particularly during the first stages of investment generation and pre-selection/pre-evaluation, are the key problems hampering EE investment financing. It is also true that often project developers do not have the expertise or resources to make a convincing financing case for investors. In order to boost EE investments, this paper proposes a Multi-Criteria Decision Analysis (MCDA) methodology intending to support financing institutions to identify attractive EE project ideas in the early development phase of project initiation and planning. The study implements the ELECTRE TRI method to benchmark EE project ideas in a standardized, investor recognizable credit rating form. A respective web-based tool facilitating the methodology and the screening of EE projects is also developed, supporting financing bodies and EE funds to rapidly detect and aggregate projects that meet the necessary criteria to be financed.

关键词： Economics Tools Benchmark testing Energy efficiency Security Standards Investment

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Incentivizing or Disincentivizing Participation in Demand Response Programs? Estimation of Customer Willingness to Accept

SSRN

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SSRN 2025年

作者： Wang, Yu Howard, Gregory Zhang, Wendong Brown, Marilyn Morton, Abigail Cetin, Kristen McCalley, James Department of Political Science Iowa State University United States Department of Economics East Carolina University United States The Charles H. Dyson School of Applied Economics and Management Cornell University United States School of Public Policy Georgia Institute of Technology United States Federal Energy Regulatory Commission United States Department of Civil and Environmental Engineering Michigan State University United States Department of Electrical and Computer Engineering Iowa State University United States

Demand response has great potential to provide value flexibility services to the grid, which can be harnessed through direct load control (DLC) programs. Many utilities have provided direct financial incentives to encourage residential customers to enroll in DLC programs. However, financial incentives do not always result in high participation rate. This study aims to assess residential customers’ willingness to enroll in utility-run DLC programs and analyze how incentives affect participation intention. A survey using the contingent valuation method (CVM) collected a random sample of 591 residents in the U.S. Midwest from 2020-2022, which finds, overall, 56.7% of residents are willing to enroll in DLC. It is estimated that 61.5% of residents are willing to participate due to intrinsic motivations (i.e., without monetary incentive), while a small token ($15-24 sign-on bonus) only leads to about 50% of the customers agreeing to enroll. To reach the same level of participation from intrinsic motivations, utilities must offer a much higher incentive - $83-$90. Additionally, we find several factors affect acceptance of DLC programs, including knowledge learning, concerns of comfort loss, and personal beliefs on energy savings, reliability, and environmental benefit. Findings from this study have significant policy implications and help improve the design of utility DLC programs. When developing strategies to recruit participants, no mention of monetary incentives could increase the participation of resident customers. We also recommend that utilities consider educational and demonstration programs to engage stakeholders, and information campaigns to emphasize the potential benefits without compromising thermal comfort. © 2025, The Authors. All rights reserved.

关键词： Sales

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SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET

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PeerJ computer Science 2025年 11卷 e2623-e2623页

作者： Abbas, Hyder Ren, Shen Bing Asim, Muhammad Hassan, Syeda Iqra El-Latif, Ahmed A. Abd State Key Laboratory of Public Big Data College of Computer Science and Technology Institute for Artificial Intelligence Guizhou University Guizhou Guiyang China School of Computer Science and Engineering Central South University Changsha China EIAS Data Science and Blockchain Laboratory College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Department of Electrical and Electronic Engineering British Malaysian Institute Universiti of Kuala Lumpur Kuala Lumpur Malaysia Software Engineering Department Sir Syed University of Engineering and Technology Karachi Pakistan Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom Egypt

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure. This model addresses a gap in previous work that focused primarily on complex backgrounds by employing a densely supervised encoder-decoder network. The proposed SODU2-NET employs sophisticated background subtraction techniques and utilizes advanced deep learning architectures that can discern relevant foreground information when dealing with complex backgrounds. Firstly, an enriched encoder block with full feature fusion (FFF) with atrous spatial pyramid pooling (ASPP) varying dilation rates to efficiently capture multi-scale contextual information, improving salient object detection in complex backgrounds and reducing the loss of information during down-sampling. Secondly the block includes an attention module that refines the decoder, is constructed to enhances the detection of salient objects in complex backgrounds by selectively focusing attention on relevant features. This allows the model to reconstruct detailed and contextually relevant information, which is essential to determining salient objects accurately. Finally, the architecture has been improved by adding a residual block at the encoder end, which is responsible for both saliency prediction and map refinement. The proposed network is designed to learn the transformation between input images and ground truth, enabling accurate segmentation of salient object regions with clear borders and accurate prediction of fine structures. SODU2-NET is demonstrated to have superior performance in five public datasets, including DUTS, SOD, DUT OMRON, HKU-IS, PASCA

关键词： Object detection

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AdCare-VLM: Leveraging Large Vision Language Model (LVLM) to Monitor Long-Term Medication Adherence and Care

arXiv

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arXiv 2025年

作者： Jabin, Md. Asaduzzaman Jiang, Hanqi Li, Yiwei Kaggwa, Patrick Douglass, Eugene Sekandi, Juliet N. Liu, Tianming School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA30602 United States School of Computing College of Engineering University of Georgia AthensGA30602 United States Department of Epidemiology and Biostatistics College of Public Health University of Georgia AthensGA30602 United States Department of Pharmaceutical and Biomedical Sciences College of Pharmacy University of Georgia AthensGA30602 United States Global Health Institute College of Public Health University of Georgia AthensGA30602 United States

Chronic diseases, including diabetes, hypertension, asthma, HIV/AIDS, epilepsy, and tuberculosis, necessitate rigorous adherence to medication to avert disease progression, manage symptoms, and decrease mortality rates. Adherence is frequently undermined by factors including patient behavior, caregiver support, elevated medical costs, and insufficient healthcare infrastructure. We propose AdCare-VLM, a specialized Video-LLaVA-based multimodal large vision language model (LVLM) aimed at visual question answering (VQA) concerning medication adherence through patient videos. We employ a private dataset comprising 806 custom-annotated tuberculosis (TB) medication monitoring videos, which have been labeled by clinical experts, to fine-tune the model for adherence pattern detection. We present LLM-TB-VQA, a detailed medical adherence VQA dataset that encompasses positive, negative, and ambiguous adherence cases. Our method identifies correlations between visual features—such as the clear visibility of the patient’s face, medication, water intake, and the act of ingestion—and their associated medical concepts in captions. This facilitates the integration of aligned visual-linguistic representations and improves multimodal interactions. Experimental results indicate that our method surpasses parameter-efficient fine-tuning (PEFT) enabled VLM models, such as LLaVA-V1.5 and Chat-UniVi, with absolute improvements ranging from 3.1% to 3.54% across pre-trained, regular, and low-rank adaptation (LoRA) configurations. Comprehensive ablation studies and attention map visualizations substantiate our approach, enhancing interpretability. © 2025, CC BY-NC-ND.

关键词： Fetal monitoring

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Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective

arXiv

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arXiv 2024年

作者： Restrepo, David Wu, Chenwei Vásquez-Venegas, Constanza Matos, João Gallifant, Jack Celi, Leo Anthony Bitterman, Danielle S. Nakayama, Luis Filipe Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Departamento de Telematica Universidad del Cauca Cauca Popayán Colombia Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Scientific Image Analysis Lab Faculty of Medicine Universidad de Chile RM Santiago Chile Department of Computer Science Faculty of Engineering Universidad de Concepción Biobio Concepción Chile Department of Biostatistics Harvard TH Chan School of Public Health BostonMA United States Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Artificial Intelligence in Medicine Program Mass General Brigham Harvard Medical School BostonMA United States Department of Ophthalmology São Paulo Federal University São Paulo São Paulo Brazil

The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient outcomes. However, the underrepresentation of diverse groups in the development and application of these models can perpetuate biases, leading to inequitable healthcare delivery. This paper presents a comprehensive scientometric analysis of LLM research for healthcare, including data from January 1, 2021, to July 1, 2024. By analyzing metadata from PubMed and Dimensions, including author affiliations, countries, and funding sources, we assess the diversity of contributors to LLM research. Our findings highlight significant gender and geographic disparities, with a predominance of male authors and contributions primarily from high-income countries (HICs). We introduce a novel journal diversity index based on Gini diversity to measure the inclusiveness of scientific publications. Our results underscore the necessity for greater representation in order to ensure the equitable application of LLMs in healthcare. We propose actionable strategies to enhance diversity and inclusivity in artificial intelligence research, with the ultimate goal of fostering a more inclusive and equitable future in healthcare innovation. © 2024, CC BY.

关键词： Decision making

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Designing Survey-Based Mobile Interfaces for Rural Patients With Cancer Using Apple's ResearchKit and CareKit: Usability Study

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JMIR Formative Research 2024年 8卷 e57801页

作者： Donawa, Alyssa Powell, Christian Wang, Rong Chih, Ming-Yuan Patel, Reema Zinner, Ralph Aronoff-Spencer, Eliah Baker, Corey E. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los Angeles CA United States Department of Computer Science University of Kentucky Lexington KY United States Department of Human and Organizational Development Vanderbilt University Nashville TN United States Department of Health and Clinical Sciences University of Kentucky Lexington KY United States Division of Medical Oncology Markey Cancer Center University of Kentucky Lexington KY United States Division of Infectious Diseases & Global Public Health School of Medicine University of California San Diego San Diego CA United States

Background: Despite the increased accessibility and availability of technology in recent years, equality and access to health-related technology remain limited to some demographics. In particular, patients who are older or from rural communities represent a large segment of people who are currently underusing mobile health (mHealth) solutions. System usability continues to hinder mHealth adoption among users with nontraditional digital literacy. Objective: This study aims to investigate if state-of-the-art mobile app interfaces from open-source libraries provide sufficient usability for rural patients with cancer, with minimal design changes and forgoing the co-design process. Methods: We developed Assuage (Network Reconnaissance Lab) as a research platform for any mHealth study. We conducted a pilot study using Assuage to assess the usability of 4 mobile user interfaces (UIs) based on open-source libraries from Apple's ResearchKit and CareKit. These UIs varied in complexity for reporting distress symptoms. Patients with cancer were recruited at the Markey Cancer Center, and all research procedures were conducted in person. Participants completed the distress assessment using a randomly selected UI in Assuage with little to no assistance. Data were collected on participant age, location, mobile app use, and familiarity with mHealth apps. Participants rated usability with the System Usability Scale (SUS), and usability issues were documented and compared. A one-way ANOVA was used to compare the effect of the UIs on the SUS scores. Results: We recruited 30 current or postsurgery patients with cancer for this pilot study. Most participants were aged >50 years (24/30, 80%), from rural areas (25/30, 83%), had up to a high school education (19/30, 63%), and were unfamiliar with mHealth apps (21/30, 70%). General mobile app use was split, with 43% (14/30) of the patients not regularly using mobile apps. The mean SUS score across the UIs was 75.8 (SD 22.2), with UI 3 and UI

关键词： apps CareKit digital divide digital literacy distress ehealth adoption ehealth literacy HealthKit mHealth mobile health patients with cancer rural health usability usability testing

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Sex-specific differences in the efficacy of traditional low frequency versus high frequency spinal cord stimulation for chronic pain

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Bioelectronic Medicine 2022年第1期8卷 1页

作者： Conic, Rosalynn R. Z. Caylor, Jacob Cui, Christina L. Reyes, Zabrina Nelson, Eric Yin, Sopyda Lerman, Imanuel Department of Family Medicine and Public Health University of California San Diego La JollaCA United States Department of Anesthesiology Center for Pain Medicine University of California San Diego School of Medicine La JollaCA United States Northwest Pain Care PS SpokaneWA United States Division of Vascular and Endovascular Surgery Department of Surgery Duke University Medical Center DurhamNC United States School of Medicine University of California San Diego San DiegoCA United States College of Osteopathic Medicine of the Pacific Western University of Health Sciences PomonaCA United States Department of Electrical and Computer Engineering University of California San Diego La JollaCA United States VA Center of Excellence for Stress and Mental Health VA San Diego Healthcare System La JollaCA United States San DiegoCA92161 United States

Introduction: Spinal cord stimulation (SCS), an FDA-approved therapy for chronic pain, uses paresthesia (low frequency SCS (LF-SCS)) or paresthesia-free (such as high-frequency SCS (HF-SCS)) systems, providing analgesia through partially-elucidated mechanisms, with recent studies indicating a sexual dimorphism in pain pathogenesis (Bretherton et al., Neuromodulation, 2021;Paller et al., Pain Med 10:289–299, 2009;Slyer et al., Neuromodulation, 2019;Van Buyten et al., Neuromodulation 20:642–649, 2017;Mekhail et al., Pain Pract, 2021). We aim to evaluate SCS therapy sex effects based on paradigm, utilizing visual analog scores (VAS), perceived pain reduction (PPR), and opioid use. Methods: A retrospective cohort study of SCS patients implanted between 2004 and 2020 (n = 237) was conducted. Descriptive statistics and linear mixed methods analyses were used. Results: HF-SCS (10 kHz) was implanted in 94 patients (40 females, 54 males), and LF-SCS in 143 (70 females, 73 males). At 3 months and 6 months, HF-SCS (p © 2022, The Author(s).

关键词： Spinal cord stimulator Outcomes HF-SCS LF-SCS VAS Opioid Sex

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Prediction of Occupational Physical Activities using Inertial Measurement Units and Deep Learning Models 64th

Prediction of Occupational Physical Activities using Inertia...

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64th International Annual Meeting of the Human Factors and Ergonomics Society, HFES 2020

作者： Yan, Yishu Fan, Hao Li, Yibin Hoeglinger, Elias Wiesinger, Alexander Barr, Alan Adamson, Carisa Harris Department of Mechanical Engineering University of California Berkeley United States Department of Industrial Design Northwestern Polytechnic China Department of Electrical Engineering and Computer Science University of California Berkeley United States University of Applied Sciences Upper Austria Linz Austria Department of Medicine University of California San Francisco United States School of Public Health University of California Berkeley United States

Many workers engaged in manual material handling (MMH) jobs experience high physical demands that are associated with work-related musculoskeletal disorders (WMSD). Quantifying the physical demands of a job is an important legal requirement in the US that is used by human resources in the job hiring process. Most physical demands analysis (PDA) are performed using observational and semi-quantitative methods. The lack of accuracy and reliability of these methods can create problems when assigning acceptable tasks to an injured worker. In this study, various deep learning models were applied to data from eight inertial measurement units (IMUs) to predict 15 occupational physical activities (OPA). Overall, a 95% accuracy was reached by convolutional neural network (CNN) for predicting occupational physical activities when performed in isolation. More work is needed to estimate the accuracy of the model when OPA elements are combined into a more complex task. © 2020 by Human Factors and Ergonomics Society. All rights reserved.

关键词： Forecasting

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