@article{earthperson_otani_nevius_prescott_diaconeasa_2023, title={A combined strategy for dynamic probabilistic risk assessment of fission battery designs using EMRALD and DEPM}, volume={160}, ISSN={0149-1970}, url={http://dx.doi.org/10.1016/j.pnucene.2023.104673}, DOI={10.1016/j.pnucene.2023.104673}, abstractNote={The notion of nuclear reactors with battery-like capabilities, called fission batteries, puts forth system requirements and design constraints that have so far been unseen in the nuclear power production industry. Such restrictions require fission batteries to be modular, integrated, autonomous, tamper-proof (i.e., resilient, fault-tolerant, all-weather, and safe), and affordable. With design requirements specifying no human intervention for operation, and minimal connectivity to remote monitoring networks, fission batteries are unique among existing nuclear power plants and emerging advanced reactor designs. Given these attributes, traditional probabilistic risk assessment (PRA) of fission batteries is expected to require dynamic methods to model advanced aspects, such as self-diagnosis, self-adjustment, and duration-prediction capabilities, as they are key ingredients for unattended operations. In addition, availability models need to integrate autonomous control, associated error-detection algorithms, and adversarial human actions. Currently, no existing framework demonstrably assesses these advanced attributes. This paper introduces and demonstrates an integrated framework for the dynamic modeling of fission battery designs. The proposed framework comprises a combined modeling strategy that uses the dual-graph error propagation methodology (DEPM) based on the continuous-time Markov chain (CTMC) models implemented in OpenPRA Error Propagation (OpenErrorPro) and the dynamic PRA tool, Event Modeling Risk Assessment using Linked Diagrams (EMRALD), based on discrete dynamic event trees (DDET). This combination overcomes some of the limitations of the tools when used independently. It enables detailed dynamic analysis to produce time explicit results to support the development of fission battery traditional PRA models. To evaluate the utility of this novel approach, a demonstration case is shown that models the hypothesized response of a fission battery design to an external fire event. DEPM CTMCs and alternative failure approaches are coupled with EMRALD to characterize and quantify the likelihood of the event sequences. The results show that the combined framework effectively captures the dynamic aspects of fission battery design in terms of the timing and realism of modeled events. Given the complexity of the failure scenarios, we believe that EMRALD and DEPM are necessary and complementary when the need for high-resolution analysis offsets the challenges of detailed modeling.}, journal={Progress in Nuclear Energy}, publisher={Elsevier BV}, author={Earthperson, Arjun and Otani, Courtney M. and Nevius, Daniel and Prescott, Steven R. and Diaconeasa, Mihai A.}, year={2023}, month={Jun}, pages={104673} }
 @inproceedings{pandit_earthperson_bao_diaconeasa_2023, title={Analyzing Hardware and Software Common Cause Failures in Digital Instrumentation and Control Systems Using Dual Error Propagation Method}, url={http://dx.doi.org/10.13182/psa23-41062}, DOI={10.13182/psa23-41062}, booktitle={18th International Probabilistic Safety Assessment and Analysis (PSA 2023)}, author={Pandit, Priyanka and Earthperson, Arjun and Bao, Han and Diaconeasa, Mihai}, year={2023}, month={Jul} }
 @article{ramezani_cao_earthperson_naeim_2023, title={Developing a Smartwatch-Based Healthcare Application: Notes to Consider}, volume={23}, ISSN={1424-8220}, url={http://dx.doi.org/10.3390/s23156652}, DOI={10.3390/s23156652}, abstractNote={Wearable devices and fitness trackers have gained popularity in healthcare and telemedicine as tools to reduce hospitalization costs, improve personalized health management, and monitor patients in remote areas. Smartwatches, particularly, offer continuous monitoring capabilities through step counting, heart rate tracking, and activity monitoring. However, despite being recognized as an emerging technology, the adoption of smartwatches in patient monitoring systems is still at an early stage, with limited studies delving beyond their feasibility. Developing healthcare applications for smartwatches faces challenges such as short battery life, wearable comfort, patient compliance, termination of non-native applications, user interaction difficulties, small touch screens, personalized sensor configuration, and connectivity with other devices. This paper presents a case study on designing an Android smartwatch application for remote monitoring of geriatric patients. It highlights obstacles encountered during app development and offers insights into design decisions and implementation details. The aim is to assist programmers in developing more efficient healthcare applications for wearable systems.}, number={15}, journal={Sensors}, publisher={MDPI AG}, author={Ramezani, Ramin and Cao, Minh and Earthperson, Arjun and Naeim, Arash}, year={2023}, month={Jul}, pages={6652} }
 @article{earthperson_diaconeasa_2023, title={Dynamic Probabilistic Risk Assessment of Commercial-Off-The-Shelf Drones in Nuclear-Contaminated Search and Rescue Missions}, url={https://doi.org/10.20944/preprints202307.0395.v1}, DOI={10.20944/preprints202307.0395.v1}, abstractNote={This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.}, author={Earthperson, Arjun and Diaconeasa, Mihai A.}, year={2023}, month={Jul} }
 @article{earthperson_diaconeasa_2023, title={Integrating Commercial-Off-The-Shelf Components into Radiation-Hardened Drone Designs for Nuclear-Contaminated Search and Rescue Missions}, volume={7}, ISSN={["2504-446X"]}, url={https://doi.org/10.3390/drones7080528}, DOI={10.3390/drones7080528}, abstractNote={This paper conducts a focused probabilistic risk assessment (PRA) on the reliability of commercial off-the-shelf (COTS) drones deployed for surveillance in areas with diverse radiation levels following a nuclear accident. The study employs the event tree/fault tree digraph approach, integrated with the dual-graph error propagation method (DEPM), to model sequences that could lead to loss of mission (LOM) scenarios due to combined hardware–software failures in the drone’s navigation system. The impact of radiation is simulated by a comparison of the total ionizing dose (TID) with the acceptable limit for each component. Errors are then propagated within the electronic hardware and software blocks to determine the navigation system’s reliability in different radiation zones. If the system is deemed unreliable, a strategy is suggested to identify the minimum radiation-hardening requirement for its subcomponents by reverse-engineering from the desired mission success criteria. The findings of this study can aid in the integration of COTS components into radiation-hardened (RAD-HARD) designs, optimizing the balance between cost, performance, and reliability in drone systems for nuclear-contaminated search and rescue missions.}, number={8}, journal={DRONES}, author={Earthperson, Arjun and Diaconeasa, Mihai A.}, year={2023}, month={Aug} }
 @phdthesis{earthperson_2023, title={Integrating Dual Error Propagation into Dynamic Event Trees to Support Fission Battery Probabilistic Risk Assessments}, school={North Carolina State University}, author={Earthperson, Arjun}, year={2023} }
 @misc{earthperson_pandit_diaconeasa_2023, place={Knoxville, TN}, title={Introducing Multiple Control Paths in the Dual Error Propagation Graph for Stochastic Failure Analysis of Digital Instrumentation and Control Systems}, url={http://dx.doi.org/10.13182/PSA23-41248}, DOI={10.13182/psa23-41248}, journal={18th International Probabilistic Safety Assessment and Analysis (PSA 2023)}, publisher={American Nuclear Society}, author={Earthperson, Arjun and Pandit, Priyanka and Diaconeasa, Mihai}, year={2023} }
 @inproceedings{earthperson_aras_farag_diaconeasa_2023, title={Introducing OpenPRA: A Web-Based Framework for Collaborative Probabilistic Risk Assessment}, url={http://dx.doi.org/10.1115/IMECE2023-111708}, DOI={10.1115/imece2023-111708}, abstractNote={
 Probabilistic Risk Assessment (PRA) tools are crucial in the nuclear, aerospace, maritime, and chemical industries. This paper presents OpenPRA, an innovative, open-source, web-based platform designed to address the limitations of current Probabilistic Risk Assessment (PRA) tools. OpenPRA offers a collaborative and flexible environment that supports a wide array of risk models and quantification engines, thereby enhancing the adaptability and efficiency of risk assessment processes. The platform offers unique features including support for various risk models such as event trees, fault trees, Markov chains, Bayesian networks, and error propagation models. It also allows users to choose from a variety of existing quantification engines, enabling them to tailor their risk assessment process to their specific needs. The paper discusses the design, development, and potential of OpenPRA to transform the field of PRA. It also delves into the platform’s technical architecture, technology stack, and the OpenPRA Model Exchange Format. The paper concludes by outlining the current development status of OpenPRA and laying out the foundation for future work.}, booktitle={Volume 13: Research Posters; Safety Engineering, Risk and Reliability Analysis}, publisher={American Society of Mechanical Engineers}, author={Earthperson, Arjun and Aras, Egemen M. and Farag, Asmaa S. and Diaconeasa, Mihai A.}, year={2023}, month={Oct} }
 @inproceedings{prins_o’connell_earthperson_alzahrani_diaconeasa_2023, title={Leveraging Probabilistic Risk Assessment and Machine Learning for Safety and Cost Optimization in HAZMAT Transportation}, url={http://dx.doi.org/10.1115/IMECE2023-114273}, DOI={10.1115/imece2023-114273}, abstractNote={
 Transportation systems play a pivotal role in modern society, but they are not without inherent risks and inefficiencies. This paper explores the integration of Probabilistic Risk Assessment (PRA) and Machine Learning (ML) techniques to enhance safety and cost optimization in hazardous materials (HAZMAT) transportation. Traditional PRA methods, while robust, are limited by the quality and quantity of data available for analysis. ML techniques can address these limitations by analyzing large datasets, identifying patterns, and making accurate predictions. The integration of ML techniques into PRA can enhance data analysis, prediction capabilities, routing decisions, resource allocation, and decision-making processes in HAZMAT transportation. This paper presents a comprehensive literature review on PRA and ML within the transportation industry, discusses the potential benefits of integrating these approaches, examines the challenges associated with transportation accident data, and suggests areas for further research and improvements in HAZMAT transportation safety analysis.}, booktitle={Volume 13: Research Posters; Safety Engineering, Risk and Reliability Analysis}, publisher={American Society of Mechanical Engineers}, author={Prins, Molly and O’Connell, Thomas M. and Earthperson, Arjun and Alzahrani, Yahya A. and Diaconeasa, Mihai A.}, year={2023}, month={Oct} }
 @inproceedings{aras_farag_earthperson_diaconeasa_2023, title={Method of Developing a SCRAM Parallel Engine for Efficient Quantification of Probabilistic Risk Assessment Models}, url={http://dx.doi.org/10.13182/psa23-41054}, DOI={10.13182/psa23-41054}, booktitle={18th International Probabilistic Safety Assessment and Analysis (PSA 2023)}, author={Aras, Egemen and Farag, Asmaa and Earthperson, Arjun and Diaconeasa, Mihai}, year={2023}, month={Jul} }
 @inproceedings{aras_farag_earthperson_diaconeasa_2023, title={Methodology and Demonstration for Performance Analysis of a Probabilistic Risk Assessment Quantification Engine: SCRAM}, url={http://dx.doi.org/10.13182/PSA23-41053}, DOI={10.13182/psa23-41053}, booktitle={18th International Probabilistic Safety Assessment and Analysis (PSA 2023)}, publisher={American Nuclear Society}, author={Aras, Egemen and Farag, Asmaa and Earthperson, Arjun and Diaconeasa, Mihai}, year={2023} }
 @inproceedings{farag_aras_earthperson_wood_boyce_diaconeasa_2023, title={Preliminary Benchmarking of SAPHSOLVE, XFTA, and SCRAM Using Synthetically Generated Fault Trees with Common Cause Failures}, url={http://dx.doi.org/10.13182/PSA23-41031}, DOI={10.13182/psa23-41031}, booktitle={18th International Probabilistic Safety Assessment and Analysis (PSA 2023)}, publisher={American Nuclear Society}, author={Farag, Asmaa and Aras, Egemen and Earthperson, Arjun and Wood, S. and Boyce, Jordan and Diaconeasa, Mihai}, year={2023} }
 @inproceedings{pandit_earthperson_nevius_diaconeasa_2023, title={Quantifying the Likelihood of Nuclear Supply Chain Shortage Risk}, url={http://dx.doi.org/10.13182/PSA23-41229}, DOI={10.13182/psa23-41229}, booktitle={18th International Probabilistic Safety Assessment and Analysis (PSA 2023)}, publisher={American Nuclear Society}, author={Pandit, Priyanka and Earthperson, Arjun and Nevius, Daniel and Diaconeasa, Mihai}, year={2023} }
 @inproceedings{aras_farag_earthperson_diaconeasa_2022, title={Benchmark Study of XFTA and SCRAM Fault Tree Solvers Using Synthetically Generated Fault Trees Models}, url={http://dx.doi.org/10.1115/IMECE2022-95783}, DOI={10.1115/imece2022-95783}, abstractNote={
 The development of the nuclear industry’s Probabilistic Risk Assessment (PRA) has significantly contributed to the design, reliability, and safety of nuclear power plants (NPP). Today, PRAs form integration in all nuclear power plant development stages, including design, licensing, operation, maintenance, and decommissioning. Many legacy PRA tools have been used in the nuclear field; however, more powerful tools are needed to cope with the rapid development of the nuclear industry and NPP designs. These tools are now required to analyze new aspects of the plants that were never envisioned before, and more computational resources are needed.
 This study uses synthetically generated fault trees of various sizes and specifications to benchmark fault tree solvers; XFTA version 1.3.1 and SCRAM version 0.16.2. The analysis is performed in two steps. The first step is to compare the probabilities, minimal cut sets, and importance factors. The second step is measuring CPU time, wall time, and the memory usage of each engine for benchmarking. The result for the first step for the same configuration is the same for each calculation engine. Overall, XFTA can complete most of the given runs in a shorter time with less memory usage than SCRAM. All computations and measurements are done on a specified computer.}, booktitle={Volume 9: Mechanics of Solids, Structures, and Fluids; Micro- and Nano-Systems Engineering and Packaging; Safety Engineering, Risk, and Reliability Analysis; Research Posters}, publisher={American Society of Mechanical Engineers}, author={Aras, Egemen M. and Farag, Asmaa S. and Earthperson, Arjun and Diaconeasa, Mihai A.}, year={2022}, month={Oct} }
 @inproceedings{yang hui otani_christian_prescott_diaconeasa_earthperson_2022, title={Probabilistic Methods for Cyclical and Coupled Systems with Changing Failure Rates}, url={https://www.osti.gov/biblio/1885929}, author={Yang Hui Otani, Courtney Mariko  ; and Christian, Robby ; and Prescott, Steven R  ; and Diaconeasa, Mihai  ; and Earthperson, Arjun}, year={2022}, month={Apr} }
 @inproceedings{ndefru_sankaran_stewart_mosleh_earthperson_zawalick_2022, title={Risk-Informed Decision-Making Tool for Covid-19 Community Behavior and Intervention Scenario Assessment}, volume={3}, ISBN={9781713863755}, booktitle={Proceedings of the 16th International Conference on Probabilistic Safety Assessment and Management (PSAM)}, publisher={Curran Associates, Inc}, author={Ndefru, Bineh and Sankaran, Karthik and Stewart, Theresa and Mosleh, Ali and Earthperson, Arjun and Zawalick, Natalie}, year={2022}, month={Jul} }
 @inproceedings{pandit_earthperson_tezbasaran_diaconeasa_2021, title={A Quantitative Approach to Assess the Likelihood of Supply Chain Shortages}, volume={13}, url={http://dx.doi.org/10.1115/IMECE2021-73696}, DOI={10.1115/imece2021-73696}, abstractNote={
 We define supply chains (SCs) as sequences of processes that link the demand and supply of goods or services within a network. SCs are prone to shortages in delivering their output goals due to several factors such as personnel undersupply, inefficient processes, policy failure, equipment malfunction, natural hazards, pandemic outbreaks, power outages, or economic crises. Recent notable supply-chain failures include the 2021 Texas power crisis, personal protection equipment shortages during the COVID-19 pandemic, and regional or global food chain shortages. The consequences of such shortages can range from negligible to devastating. The Texas power crisis resulted in the death of 70 people and left approximately 4.5 billion homes and businesses without power for multiple days.
 In this paper, we presented a methodology to quantify the failure probability of the throughput of a supply chain. We divided the methodology into two major categories of steps. In the first step, we converted the given or assumed supply chain data into fault trees and quantify them. In the second step, we iterated the quantification of the fault tree to build a supply chain shortage risk profile. We introduced the notion of success criteria for the output from a facility, based on which we included or excluded the facility for quantification.
 With the inclusion of relevant field data, we believe that our methodology can enable the stakeholders in the supply-chain decision-making process to detect vulnerable facilities and risk-inform prevention and mitigation actions. Applications for this methodology can include construction, inventory stocking, assessing manufacturing quantities, policy changes, personnel allocation, and financial investment for critical industries such as nuclear, pharmaceutical, aviation, etc.}, booktitle={Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters}, publisher={American Society of Mechanical Engineers}, author={Pandit, Priyanka and Earthperson, Arjun and Tezbasaran, Alp and Diaconeasa, Mihai A.}, year={2021}, month={Nov} }
 @inproceedings{pandit_tezbasaran_earthperson_diaconeasa_2021, title={Evaluating the Implementation of Distributed Ledger Technology for the Licensing and Regulation of Nuclear Power Plants}, volume={8B-2021}, url={http://dx.doi.org/10.1115/IMECE2021-71730}, DOI={10.1115/imece2021-71730}, abstractNote={
 The approval process from the U.S. Nuclear Regulatory Commission (NRC) for nuclear power plants is sequential. It involves several government bodies such as the Advisory Committee on Reactor Safeguards (ACRS), public meetings, and hearings. If the submissions made to the NRC do not contain enough information to meet the regulation requirements, the NRC issues a Request for Additional Information (RAI). Thus, the licensee has to go through a paperwork-intensive process that involves multiple regulatory agencies for the various licensing requirements. Moreover, sending applications to the NRC is limited to using an electronic submission generation tool called the Packing Slip Wizard (PSW).
 This paper presents a methodology to implement Distributed Ledger Technology (DLT) to address the need for a real-time, digitized documentation platform in the nuclear power industry’s licensing and regulation process.
 The evaluation of DLT’s implementation resulted in the formulation of a methodology to accept submissions from an applicant on a web application and storing the received data on a distributed ledger. The presented method offers a real-time submission of the available information of an application. It facilitates the NRC with a real-time feedback capability expediting the review process. RAI’s can be reduced in number by ensuring that the NRC’s information requirements are defined as smart contracts.}, booktitle={Volume 8B: Energy}, publisher={American Society of Mechanical Engineers}, author={Pandit, Priyanka and Tezbasaran, Alp and Earthperson, Arjun and Diaconeasa, Mihai A.}, year={2021}, month={Nov} }
 @article{rabiei_huang_chien_earthperson_diaconeasa_woo_iyer_white_mosleh_2021, title={Method and software platform for electronic COTS parts reliability estimation in space applications}, volume={235}, ISSN={1748-006X 1748-0078}, url={http://dx.doi.org/10.1177/1748006X21998231}, DOI={10.1177/1748006X21998231}, abstractNote={Adoption of electronic Commercial-Off-The-Shelf (COTS) parts in various industrial products is rapidly increasing due to the accessibility and appealing lower cost of these commodities. Depending on the type of application, having an accurate understanding of the COTS failure information can be crucial to ensure the reliability and safety of the final products. On the other hand, frequent large-scale testing is often cost prohibitive and time consuming for emerging technologies, especially in the consumer electronics sector where minimizing time-to-market and cost is critical. This paper presents a comprehensive Bayesian approach and software platform (named COTS Reliability Expert System), that integrates multiple pieces of heterogeneous information about the failure rate of COTS parts. The ultimate goal is to reduce dependency on testing for reliability analysis and yet to obtain a more accurate “order of magnitude” estimate of the failure rate through an efficient process. The method provides a foundation for incorporating manufacturers reliability data, estimates based on underlying physics-of-failure mechanisms and circuit simulations, partially relevant life test data of similar (but not necessarily identical) parts, and expert opinions on the manufacturing process of the COTS part of interest. The developed expert system uses Bayesian estimation to integrate all these types of evidence. The methodology is demonstrated in estimating the failure rate of a static random-access memory (SRAM) part.}, number={5}, journal={Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability}, publisher={SAGE Publications}, author={Rabiei, Elaheh and Huang, Lixian and Chien, Hao-Yu and Earthperson, Arjun and Diaconeasa, Mihai A and Woo, Jason and Iyer, Subramanian and White, Mark and Mosleh, Ali}, year={2021}, month={Mar}, pages={744–760} }
 @misc{earthperson_diaconeasa_2021, title={Verification Study of the Nuclear PRA for the Mars 2020 Mission Following Accidental Orbital Re-Entry}, volume={13}, url={http://dx.doi.org/10.1115/IMECE2021-71359}, DOI={10.1115/imece2021-71359}, abstractNote={
 Today, Probabilistic Risk Assessment (PRA) plays a vital role in assuring mission success for robotic and crewed missions alike. Current-day PRA techniques integrate multimodal, often black-box analyses to build comprehensive risk profiles. This paper describes a review and verification study of the “Nuclear Risk Assessment for the Mars 2020 Mission Environmental Impact Statement” (N-PRA)[1]. Sandia National Labs conducted the N-PRA for NASA’s Jet Propulsion Laboratory (JPL). More specifically, we have verified the source term calculations associated with the release of radionuclides from a Multi-Mission Radiothermoelectic Generator (MMRTG) power source for a limited set of accident scenarios in the case of an accidental re-entry into Earth Orbit with an Earth impacting trajectory.
 We achieve this by using analytical methods[2] historically implemented for the Cassini Mission PRA[3] for a failed planetary swingby gravity-assist. Our results are within 28% to 56% of the referenced study. Limitations in our methodology are attributed to a lack of modern simulation-based tools and deterministic methods for modeling complex physical phenomena. The results are interpreted and compared with the values presented by the initial authors, along with comments for improving our current methodology.}, journal={Volume 13: Safety Engineering, Risk, and Reliability Analysis; Research Posters}, publisher={American Society of Mechanical Engineers}, author={Earthperson, Arjun and Diaconeasa, Mihai A.}, year={2021}, month={Nov} }
 @book{ramezani_moatamed_arjun_naeim_sarrafzadeh_2018, title={Subject assessment using localization, activity recognition and a smart questionnaire}, number={20180190382}, author={Ramezani, Ramin and Moatamed, Babek and Arjun and Naeim, Arash and Sarrafzadeh, Majid}, year={2018} }
 @phdthesis{earthperson_2017, title={DSP optimization techniques for LCDK with focus on IoT applications}, url={http://rgdoi.net/10.13140/RG.2.2.20822.40008}, DOI={10.13140/RG.2.2.20822.40008}, author={Earthperson, Arjun}, year={2017} }
 @inproceedings{malavalli_arjun_gupta_2017, title={Indoor Localization Through Machine Learning on WiFi Fingerprints}, ISBN={9781509062980}, author={Malavalli, R. and Arjun and Gupta, Nilesh}, year={2017}, month={Sep} }
 @inproceedings{bouchard_ramezani_arjun_naeim_2016, title={Evaluation of Bluetooth beacons behavior}, ISBN={9781509014965}, url={http://dx.doi.org/10.1109/uemcon.2016.7777846}, DOI={10.1109/uemcon.2016.7777846}, abstractNote={Bluetooth low energy beacons have gained traction among ambient intelligence researchers. Their low cost and robustness make them a fit choice for ambient assisted living or other healthcare applications. Nevertheless, similar to many other radio-frequency based technologies, beacons pose serious challenges when it comes to developing applications that require predictability. Moreover, the multipath propagation characteristics of Bluetooth beacons prevent ambient intelligent researchers to solely rely on using theoretical models to describe their behaviors. In this paper, we present an empirical evaluation of Bluetooth beacons behaviors. We describe the observed pattern of the signal through a series of experiments totalizing more than 3 million samples.}, booktitle={2016 IEEE 7th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)}, publisher={IEEE}, author={Bouchard, Kevin and Ramezani, Ramin and Arjun and Naeim, Arash}, year={2016}, month={Oct} }
 @book{naeim_ramezani_arjun_moatamed_sarrafzadeh_2016, title={Indoor Health Monitoring System}, number={62/330,730}, author={Naeim, A. and Ramezani, R. and Arjun and Moatamed, B. and Sarrafzadeh, M.}, year={2016}, month={May} }
 @inproceedings{moatamed_arjun_shahmohammadi_ramezani_naeim_sarrafzadeh_2016, title={Low-cost indoor health monitoring system}, ISBN={9781509030873}, url={http://dx.doi.org/10.1109/bsn.2016.7516252}, DOI={10.1109/bsn.2016.7516252}, abstractNote={The advent of smart infrastructure or Internet of Things (IoT) has enabled scenarios in which objects with unique identifiers can communicate and transfer data over a network without human to human/computer interactions. Incorporating hardware in such networks is so cheap that it has opened the possibility of connecting just about anything from simple nodes to complex, remotely-monitored sensor networks. In the paper, we describe a low-cost scalable and potentially ubiquitous system for indoor remote health monitoring using low energy bluetooth beacons and a smartwatch. Our system was implemented in a rehabilitation facility in Los Angeles and the overall assessments revealed promising results.}, booktitle={2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)}, publisher={IEEE}, author={Moatamed, Babak and Arjun and Shahmohammadi, Farhad and Ramezani, Ramin and Naeim, Arash and Sarrafzadeh, Majid}, year={2016}, month={Jun} }