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732 results on '"Karimi, Mohsen"'

1. CARTOS: A Charging-Aware Real-Time Operating System for Intermittent Batteryless Devices

Author

Karimi, Mohsen, Wang, Yidi, Kim, Youngbin, Lim, Yoojin, and Kim, Hyoseung

Subjects
Computer Science - Operating Systems, Electrical Engineering and Systems Science - Systems and Control
Abstract

This paper presents CARTOS, a charging-aware real-time operating system designed to enhance the functionality of intermittently-powered batteryless devices (IPDs) for various Internet of Things (IoT) applications. While IPDs offer significant advantages such as extended lifespan and operability in extreme environments, they pose unique challenges, including the need to ensure forward progress of program execution amidst variable energy availability and maintaining reliable real-time time behavior during power disruptions. To address these challenges, CARTOS introduces a mixed-preemption scheduling model that classifies tasks into computational and peripheral tasks, and ensures their efficient and timely execution by adopting just-in-time checkpointing for divisible computation tasks and uninterrupted execution for indivisible peripheral tasks. CARTOS also supports processing chains of tasks with precedence constraints and adapts its scheduling in response to environmental changes to offer continuous execution under diverse conditions. CARTOS is implemented with new APIs and components added to FreeRTOS but is designed for portability to other embedded RTOSs. Through real hardware experiments and simulations, CARTOS exhibits superior performance over state-of-the-art methods, demonstrating that it can serve as a practical platform for developing resilient, real-time sensing applications on IPDs.

Published
2023

7. Machine Learning Based Framework for Estimation of Data Center Power Using Acoustic Side Channel

Author

Karimi, Mohsen and Arab, Fahimeh

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Data centers are high power consumers and the energy consumption of data centers keeps on rising in spite of all the efforts for increasing the energy efficiency. The need for energy-awareness in data centers makes the use of power modeling and estimation to be still a big challenge due to huge amount of uncertainty in this area. In this paper, a machine learning based method is proposed to approximately estimate the amount of power consumption by using acoustic side channel caused by fan in the fan-based cooling system in the server room. For doing so, frequency components of the acoustic signal, recorded by a microphone in the server room, is extracted, pre-processed, and fed to a Multi-Layer Neural-Network as an estimator. The proposed method performed well to estimate the power consumption, having more than 85 percent accuracy.

Published
2020

11. De novo Damaging Variants, Clinical Phenotypes and Post-Operative Outcomes in Congenital Heart Disease

Author

Boskovski, Marko T, Homsy, Jason, Nathan, Meena, Sleeper, Lynn A, Morton, Sarah, Manheimer, Kathryn B, Tai, Angela, Gorham, Joshua, Lewis, Matthew, Swartz, Michael, Alfieris, George M, Bacha, Emile A, Karimi, Mohsen, Meyer, David, Nguyen, Khanh, Bernstein, Daniel, Romano-Adesman, Angela, Porter, George A, Goldmuntz, Elizabeth, Chung, Wendy K, Srivastava, Deepak, Kaltman, Jonathan R, Tristani-Firouzi, Martin, Lifton, Richard, Roberts, Amy E, Gaynor, J William, Gelb, Bruce D, Kim, Richard, Seidman, Jonathan G, Brueckner, Martina, Mayer, John E, Newburger, Jane W, and Seidman, Christine E

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Genetics, Patient Safety, Congenital Structural Anomalies, Clinical Research, Human Genome, Transplantation, Heart Disease, Cardiovascular, Pediatric, Aetiology, 2.1 Biological and endogenous factors, Good Health and Well Being, Adolescent, Child, Child, Preschool, Chromosomes, Human, Pair 15, Chromosomes, Human, Pair 3, DNA Copy Number Variations, Female, Heart Defects, Congenital, Heart Transplantation, Humans, Infant, Kaplan-Meier Estimate, Machine Learning, Male, Odds Ratio, Phenotype, Proportional Hazards Models, Exome Sequencing, genomics, congenital heart disease, heart transplantation, mortality, survival, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology
Abstract

BackgroundDe novo genic and copy number variants are enriched in patients with congenital heart disease, particularly those with extra-cardiac anomalies. The impact of de novo damaging variants on outcomes following cardiac repair is unknown.MethodsWe studied 2517 patients with congenital heart disease who had undergone whole-exome sequencing as part of the CHD GENES study (Congenital Heart Disease Genetic Network).ResultsTwo hundred ninety-four patients (11.7%) had clinically significant de novo variants. Patients with de novo damaging variants were 2.4 times more likely to have extra-cardiac anomalies (P=5.63×10-12). In 1268 patients (50.4%) who had surgical data available and underwent open-heart surgery exclusive of heart transplantation as their first operation, we analyzed transplant-free survival following the first operation. Median follow-up was 2.65 years. De novo variants were associated with worse transplant-free survival (hazard ratio, 3.51; P=5.33×10-04) and longer times to final extubation (hazard ratio, 0.74; P=0.005). As de novo variants had a significant interaction with extra-cardiac anomalies for transplant-free survival (P=0.003), de novo variants conveyed no additional risk for transplant-free survival for patients with these anomalies (adjusted hazard ratio, 1.96; P=0.06). By contrast, de novo variants in patients without extra-cardiac anomalies were associated with worse transplant-free survival during follow-up (hazard ratio, 11.21; P=1.61×10-05) than that of patients with no de novo variants. Using agnostic machine-learning algorithms, we identified de novo copy number variants at 15q25.2 and 15q11.2 as being associated with worse transplant-free survival and 15q25.2, 22q11.21, and 3p25.2 as being associated with prolonged time to final extubation.ConclusionsIn patients with congenital heart disease undergoing open-heart surgery, de novo variants were associated with worse transplant-free survival and longer times on the ventilator. De novo variants were most strongly associated with adverse outcomes among patients without extra-cardiac anomalies, suggesting a benefit for preoperative genetic testing even when genetic abnormalities are not suspected during routine clinical practice. Registration: URL: https://www.clinicaltrials.gov. Unique identifier: NCT01196182.

Published
2020

23. Enabling Reliable Batteryless Real-Time Sensing

Author

Karimi, Mohsen

Subjects
Electrical engineering, Computer science, Computer engineering
Abstract

The emergence of intermittently-powered batteryless devices (IPDs) for energy harvesting in harsh environments has spurred research into addressing their challenges. This dissertation integrates and extends three key contributions in the realm of real-time sensing and task scheduling for such devices. First, a scheduling framework prioritizing atomic sensing operations for periodic execution on IPDs is proposed, ensuring efficient utilization of intermittent power sources and verifying task schedulability under varied charging conditions. Secondly, a novel energy-adaptive real-time sensing framework is introduced, leveraging lightweight machine learning to predict energy availability and adjust task schedules to ensure continuous sensing operations while meeting data freshness requirements. Thirdly, CARTOS, a charging-aware real-time operating system for IPDs, is presented, employing mixed-preemption scheduling and just-in-time checkpointing to ensure both computational and peripheral tasks execute efficiently amidst energy variability while supporting processing chains with adaptable scheduling.These contributions collectively address the challenges of ensuring continuous operation, data freshness, and reliable execution on IPDs in IoT applications. Evaluations across real hardware experiments and simulations demonstrate the superiority of the proposed frameworks over existing methods. Overall, these advancements offer practical solutions for developing resilient real-time sensing applications on batteryless devices, paving the way for their widespread deployment in diverse scenarios.

Published
2023

38. Contributors

Author

Abdelrehim, Osama, primary, Balegh, Yaser, additional, Biniaz, Parisa, additional, Corrente, Giuseppina Anna, additional, Dalena, Francesco, additional, Esfandyari, Morteza, additional, Farsi, Mohammad, additional, Fouladi, Niloufar, additional, Gholami, Mohammad, additional, Hafizi, Ali, additional, Hamidpour, Sareh, additional, Hassanzadeh, Ahmad, additional, Hosseini, Hamid Reza, additional, Kamali, Reza, additional, Karimi, Mohsen, additional, Khojasteh, Danial, additional, Khorshidian, Hossein, additional, Makarem, Mohammad Amin, additional, Manshadi, Mohammad K.D., additional, Mohammadi, Mehdi, additional, Mohammadi, Mohammad Hossein, additional, Monfared, Leila Karami, additional, Moravvej, Zohre, additional, Naseh, Mozhgan, additional, Nikookar, Hamed, additional, Olivito, Fabrizio, additional, Paone, Emilia, additional, Piroozmand, Mehdi, additional, Rahimpour, Mohammad Reza, additional, Rodrigues, Alírio E., additional, Roostaie, Tayebe, additional, Salimian, Farideh, additional, Sanati-Nezhad, Amir, additional, Sedghamiz, Mohammad Amin, additional, Senatore, Alessandro, additional, Shirazi, Nazanin Abrishami, additional, Silva, José A.C., additional, Soroush, Ebrahim, additional, Talebi, Shahram, additional, Tursi, Antonio, additional, Vakylabad, Ali Behrad, additional, Verteramo, Federica, additional, and Yesiloz, Gurkan, additional

Published
2021
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47. Energy Scheduling for Task Execution on Intermittently-Powered Devices

Author

Karimi, Mohsen

Subjects
Electrical engineering
Abstract

Intermittently-powered embedded devices are getting widespread attention these days. However, running real-time tasks on these devices remains a challenging problem due to the lack of support for data freshness guarantee, time keeping, and schedulability analysis. Furthermore, while many sensing applications require low-level sensor readings to be done in an atomic way, meaning that the operations cannot be suspended and resumed later, existing solutions for intermittently-powered devices assume compute-only workloads and disregard such sensor operations. In this research, we provide an energy harvesting model for intermittently-powered devices, and based on that, propose techniques to utilize intermittent power sources efficiently and to schedule real-time periodic tasks that need atomic operations. We present a hardware-software co-design scheme to keep track of time and to ensure the periodic execution of sensing tasks. We provide schedulability analysis to determine if a single task is schedulable in a given charging setup, and extend this idea to scheduling multiple tasks. As a proof-of-concept, we design a custom programmable RFID tag device, called R'tag, and demonstrate the effect of our proposed techniques in a realistic sensing application. We also show the device parameters' effect on energy harvesting performance in simulation. We compare the baseline approach and the proposed method both in simulation and experimental evaluations. Evaluation results, both on simulation and experiment, verify that the proposed method outperforms the baseline approach in terms of task scheduling, time keeping, and periodic sensing.

Published
2021

49. Separation of CO2/N2onto Shaped MOF MIL-160(Al) Using the Pressure Swing Adsorption Process for Post-combustion Application

Author

Karimi, Mohsen, Siqueira, Rafael M., Rodrigues, Alírio E., Nouar, Farid, Silva, José A. C., Serre, Christian, and Ferreira, Alexandre F. P.

Abstract

Adsorption processes have already been considered as an appealing technology for carbon capture and climate change mitigation. Accordingly, this work investigated the capacity of shaped MIL-160(Al) as a water stable bioderived Al dicarboxylate microporous metal–organic framework for separation of carbon dioxide and nitrogen concerning postcombustion application. First, breakthrough experiments of carbon dioxide and nitrogen were accomplished at 313 K and 4.0 bar. Then, a set of equations/relations were considered to model the dynamic fixed-bed tests, in which the outcomes proved the capacity of the developed model for such a purpose. Next, a pressure swing adsorption (PSA) process with five steps, including pressurization, feed, rinse, blowdown, and purge, was planned and validated using performed experiments in a laboratory-scale PSA setup. In the end, an industrial PSA process was designed to attain a better grasp of the capacity of MIL-160(Al) for postcombustion application. The results indicated an exciting potential of this adsorbent for postcombustion carbon capture, with the purity and recovery of carbon dioxide around 67.3 and 99.1%, respectively.

Published
2024
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50. Gene expression and risk of leukemic transformation in myelodysplasia

Author

Shiozawa, Yusuke, Malcovati, Luca, Gallì, Anna, Pellagatti, Andrea, Karimi, Mohsen, Sato-Otsubo, Aiko, Sato, Yusuke, Suzuki, Hiromichi, Yoshizato, Tetsuichi, Yoshida, Kenichi, Shiraishi, Yuichi, Chiba, Kenichi, Makishima, Hideki, Boultwood, Jacqueline, Hellström-Lindberg, Eva, Miyano, Satoru, Cazzola, Mario, and Ogawa, Seishi

Published
2017
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