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1. Towards Continuous Skin Sympathetic Nerve Activity Monitoring: Removing Muscle Noise

Author

Baghestani, Farnoush, Nejad, Mahdi Pirayesh Shirazi, Kong, Youngsun, and Chon, Ki H.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Neurons and Cognition
Abstract

Continuous monitoring of non-invasive skin sympathetic nerve activity (SKNA) holds promise for understanding the sympathetic nervous system (SNS) dynamics in various physiological and pathological conditions. However, muscle noise artifacts present a challenge in accurate SKNA analysis, particularly in real-life scenarios. This study proposes a deep convolutional neural network (CNN) approach to detect and remove muscle noise from SKNA recordings obtained via ECG electrodes. Twelve healthy participants underwent controlled experimental protocols involving cognitive stress induction and voluntary muscle movements, while collecting SKNA data. Power spectral analysis revealed significant muscle noise interference within the SKNA frequency band (500-1000 Hz). A 2D CNN model was trained on the spectrograms of the data segments to classify them into baseline, stress-induced SKNA, and muscle noise-contaminated periods, achieving an average accuracy of 89.85% across all subjects. Our findings underscore the importance of addressing muscle noise for accurate SKNA monitoring, advancing towards wearable SKNA sensors for real-world applications., Comment: 4 pages, 5 figures, 1 table, IEEE-EMBS International Conference on Body Sensor Networks: NextGen Health: Sensor Innovation, AI, and Social Responsibility (IEEE BSN 2024)

Published
2024

2. Dynamic operator management in meta-heuristics using reinforcement learning: an application to permutation flowshop scheduling problems

Author

Mamaghan, Maryam Karimi, Mohammadi, Mehrdad, Dullaert, Wout, Vigo, Daniele, and Pirayesh, Amir

Subjects
Computer Science - Machine Learning
Abstract

This study develops a framework based on reinforcement learning to dynamically manage a large portfolio of search operators within meta-heuristics. Using the idea of tabu search, the framework allows for continuous adaptation by temporarily excluding less efficient operators and updating the portfolio composition during the search. A Q-learning-based adaptive operator selection mechanism is used to select the most suitable operator from the dynamically updated portfolio at each stage. Unlike traditional approaches, the proposed framework requires no input from the experts regarding the search operators, allowing domain-specific non-experts to effectively use the framework. The performance of the proposed framework is analyzed through an application to the permutation flowshop scheduling problem. The results demonstrate the superior performance of the proposed framework against state-of-the-art algorithms in terms of optimality gap and convergence speed.

Published
2024

5. Effect of Epigallocatechin-3-gallate (EGCG) on cell proliferation inhibition and apoptosis induction in lymphoblastic leukemia cell line

Author

Ghasemi-Pirbaluti Masome, Pourgheysari Batool, Shirzad Hedayatollah, Motaghi Ehsan, Askarian Dehkordi Narges, Surani Zahra, Shirzad Moein, Beshkar Pezhman, and Pirayesh Ashkan

Subjects
EGCG, Proliferation, Jurkat cell line, Acute lymphoblastic leukemia, Apoptosis, Medicine (General), R5-920, Therapeutics. Pharmacology, RM1-950
Abstract

Introduction: Acute lymphoblastic leukemia (ALL) is one of the malignant proliferations of lymphoid cells in the early stages of differentiation and accounts for ¾ of all cases of childhood leukemia. Available treatment cannot completely treat this disease. Epigallocatechin-3-gallate (EGCG) is a polyphenolic compounds in the green tea that has demonstrated to have anticancer and antimitotic properties. The purpose of the present study was the evaluation of the effect of EGCG on the proliferation inhibition and apoptosis induction in a lymphoblastic leukemia cell line. Methods: Jurkat cell line was cultured in standard condition and in different concentrations of EGCG (0-100 micromolar) for 24, 48 and 72 hours. Cell viability was measured by MTS assay. Apoptosis induction was assessed by annexin V-FITC and flow cytometry analysis. Results: The MTS assay revealed that EGCG has decreased cell viability with a time and dose dependent manner. The level of cell apoptosis in all used concentrations of EGCG (50, 70 and 100 μm) was higher than control group (71%, 40% and 31% respectively vs. 8%) and reached to significant level at 100 μm concentration. Conclusion: The study indicated that EGCG is effective on proliferation inhibition and apoptotic induction in Jurkat lymphoblastic cell line. Therefore, the study of the mechanism of apoptosis induction could be a step of progress toward target therapy which might be considered in the future studies.

Published
2015

7. Internal Dosimetry in Patients Undergoing Peptide Receptor Radionuclide Therapy (PRRT) with 177Lu-[DOTA0-Tyr3] Octerotate: A Single-Center Experience

Author

Elahe Mahmoudi, Mahasti Amoui, Mohammad Reza Deevband, Elahe Pirayesh, Mohammad Reza Asgari, and Mehdi Ghorbani

Subjects
Individualized Dosimetry, 177Lu-DOTA0, Tyr3-Octreotate, Neuroendocrine Tumors, Organ at Risk, Medical technology, R855-855.5
Abstract

Purpose: Peptide Receptor Radionuclide Therapy (PRRT) with 177Lu-DOTATATE (177 Lu DOTA-TATE therapy is a form of PRRT which targets Somatostatin Receptors (SSR). It is a form of targeted drug delivery, which is applicable to treat neuroendocrine tumors. PRRT applications are continuously expanding in most departments of nuclear medicine in Iran, but the best of all, no one has studied the mean doses of organs of the patients. This research aims to specify the absorbed dose to patients for the treatment of neuroendocrine tumors using imaging with 177Lu-[DOTA0-Tyr3] octerotate. Materials and Methods: Whole body planar scintigraphy images were collected for 10 patients, which are used as the basis for the personalized patient dosimetry calculations. The patients had a mean age of 53.5 ± 12 years (ranging from 36 to 70 years) and imaging data were collected at roughly 0 to 2 hours, 4 to 6 hours, 18 to 24 hours, and 36 to 48 hours after the injection of 6401± 628.4 MBq (range of 5500 MBq-7400 MBq) of 177Lu-[DOTA0-Tyr3] octerotate. Models of time-activity were established for different organs. Finally, using absorbed dose formulation and IDIAC-Dosage software, the mean absorbed dose in the organs was determined. Results: Mean calculated dose in the kidney and liver were obtained as 0.30-0.82 mGy/ MBq, and 1.05-2.11 mGy/MBq, respectively. Conclusion: Based on the results, PRRT therapy is a safe method for the treatment of castration-resistant neuroendocrine cancer patients in terms of patient dose. Large inter-individual differences in organ dose were discovered, highlighting the importance of patient-specific dosimetry and treatment planning in the treatment with 177Lu-DOTATATE.

Published
2025
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8. Utilization of Mean Platelet Volume for Predicting Ischemic Heart Disease in Diabetic and Non-Diabetic Patients

Author

Maryam Arefnia, Toktam Hosseinnezhad Ariani, Mohammadali Ghodsirad, Toktam Alirezaei, Elinaz Hosseinzadeh, Mahesti Amoui, Elaheh Pirayesh, and Ghazaal Norouzi

Subjects
mean platelet volume, myocardial perfusion scan, diabetes mellitus, coronary heart disease, Medicine (General), R5-920
Abstract

Background: This study aimed to evaluate the relationship between mean platelet volume (MPV) and myocardial perfusion abnormalities in patients with and without Type 2 diabetes mellitus (DM) using myocardial perfusion scans. Materials and Methods: This cross-sectional study compared 49 patients with Type 2 DM without overt cardiovascular symptoms with 49 healthy controls. Both groups underwent myocardial perfusion scans at rest and under stress conditions. Risk factors were assessed and recorded using a special research-made questionnaire. A complete blood count and MPV results were obtained using the Sysmex - KX-21 system. Data were analyzed using SPSS, with a p-value below 0.05 considered statistically significant. Results: No significant differences were observed between the two groups in terms of Summed Stress Score (SSS), Summed Rest Score (SRS), Summed Difference Score (SDS), Ejection Fraction (EF), and End Systolic Volume (ESV). The only marked variance was a higher average platelet count in the control group. Regression analysis revealed that a one-unit increase in MPV correlated with a 0.46 average increase in SRS in the control group (CI: 0.08-0.83, β: 0.46). Conclusion: MPV may serve as a predictive marker for myocardial perfusion abnormalities, especially in individuals without diabetes. This simple metric could act as an early indicator for coronary artery disease.

Published
2025
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9. Explaining Exchange Rate Forecasts with Macroeconomic Fundamentals Using Interpretive Machine Learning

Author

Neghab, Davood Pirayesh, Cevik, Mucahit, and Wahab, M. I. M.

Subjects
Quantitative Finance - Statistical Finance, Computer Science - Machine Learning
Abstract

The complexity and ambiguity of financial and economic systems, along with frequent changes in the economic environment, have made it difficult to make precise predictions that are supported by theory-consistent explanations. Interpreting the prediction models used for forecasting important macroeconomic indicators is highly valuable for understanding relations among different factors, increasing trust towards the prediction models, and making predictions more actionable. In this study, we develop a fundamental-based model for the Canadian-U.S. dollar exchange rate within an interpretative framework. We propose a comprehensive approach using machine learning to predict the exchange rate and employ interpretability methods to accurately analyze the relationships among macroeconomic variables. Moreover, we implement an ablation study based on the output of the interpretations to improve the predictive accuracy of the models. Our empirical results show that crude oil, as Canada's main commodity export, is the leading factor that determines the exchange rate dynamics with time-varying effects. The changes in the sign and magnitude of the contributions of crude oil to the exchange rate are consistent with significant events in the commodity and energy markets and the evolution of the crude oil trend in Canada. Gold and the TSX stock index are found to be the second and third most important variables that influence the exchange rate. Accordingly, this analysis provides trustworthy and practical insights for policymakers and economists and accurate knowledge about the predictive model's decisions, which are supported by theoretical considerations.

Published
2023

13. State-dependent Asset Allocation Using Neural Networks

Author

Bradrania, Reza and Neghab, Davood Pirayesh

Subjects
Quantitative Finance - General Finance, G.3, G.1, I.2
Abstract

Changes in market conditions present challenges for investors as they cause performance to deviate from the ranges predicted by long-term averages of means and covariances. The aim of conditional asset allocation strategies is to overcome this issue by adjusting portfolio allocations to hedge changes in the investment opportunity set. This paper proposes a new approach to conditional asset allocation that is based on machine learning; it analyzes historical market states and asset returns and identifies the optimal portfolio choice in a new period when new observations become available. In this approach, we directly relate state variables to portfolio weights, rather than firstly modeling the return distribution and subsequently estimating the portfolio choice. The method captures nonlinearity among the state (predicting) variables and portfolio weights without assuming any particular distribution of returns and other data, without fitting a model with a fixed number of predicting variables to data and without estimating any parameters. The empirical results for a portfolio of stock and bond indices show the proposed approach generates a more efficient outcome compared to traditional methods and is robust in using different objective functions across different sample periods.

Published
2022
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16. A Layered Semantic Interoperability Framework for Conflict Resolution of Semantic Models in Smart Devices

Author

Mofatteh, Mohammad Yaser, Pirayesh, Amir, Fatahi Valilai, Omid, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2024
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19. Identifying Barriers to the Participation of Diabetic Patients in Khuzestan Province in Sports Activities

Author

Asadollah Pirayesh, Sedighe Heydarinejad, and Esmaeel Veisia

Subjects
diabetes, sport activities, insulin resistance, khuzestan, Medicine
Abstract

Objective: Exercise and physical activity as an essential factor and an integral part in the prevention of standard management of diabetes, however, most people with diabetes are not active enough. The objective of this research was to identify the obstacles hindering the participation of diabetic patients in sports activities in Khuzestan province. Materials and Methods: The study followed an exploratory-applicative approach, utilizing a mixed qualitative-quantitative methodology. The qualitative section involved 18 knowledgeable experts, while the quantitative section included all diabetes patients in Khuzestan province. The sample size for the quantitative section was determined to be 384 patients, based on Morgan's table. Data collection in the qualitative part was conducted through semi-structured interviews, resulting in the extraction of 46 concepts, 19 sub-categories, and 8 main categories over three stages. In the quantitative part, a researcher-designed questionnaire was used, based on the interview findings. The reliability of the questionnaire was assessed using Cronbach's alpha coefficient, which yielded a value of 0.90. Data analysis was performed using SPSS and Smart PLS software. Results: The findings indicated that various factors act as barriers to the participation of diabetic patients in sports activities, including self-actualization (7.79), environmental (6.10), educational and research (5.83), managerial (5.72), cultural (2.85), physical and supportive (2.8), time (2.62), and economic (2.15) factors. Conclusion: Therefore, it is recommended that sports and health officials in the province take measures to alleviate these obstacles and improve the quality of life for diabetic patients.

Published
2024

20. A multi-objective constrained POMDP model for breast cancer screening

Author

Helmeczi, Robert K., Kavaklioglu, Can, Cevik, Mucahit, and Neghab, Davood Pirayesh

Subjects
Computer Science - Artificial Intelligence
Abstract

Breast cancer is a common and deadly disease, but it is often curable when diagnosed early. While most countries have large-scale screening programs, there is no consensus on a single globally accepted guideline for breast cancer screening. The complex nature of the disease; the limited availability of screening methods such as mammography, magnetic resonance imaging (MRI), and ultrasound; and public health policies all factor into the development of screening policies. Resource availability concerns necessitate the design of policies which conform to a budget, a problem which can be modelled as a constrained partially observable Markov decision process (CPOMDP). In this study, we propose a multi-objective CPOMDP model for breast cancer screening which allows for supplemental screening methods to accompany mammography. The model has two objectives: maximize the quality-adjusted life years (QALYs) and minimize lifetime breast cancer mortality risk (LBCMR). We identify the Pareto frontier of optimal solutions for average and high-risk patients at different budget levels, which can be used by decision-makers to set policies in practice. We find that the policies obtained by using a weighted objective are able to generate well-balanced QALYs and LBCMR values. In contrast, the single-objective models generally sacrifice a substantial amount in terms of QALYs/LBCMR for a minimal gain in LBCMR/QALYs. Additionally, our results show that, with the baseline cost values for supplemental screenings as well as the additional disutility that they incur, they are rarely recommended in CPOMDP policies, especially in a budget-constrained setting. A sensitivity analysis reveals the thresholds on cost and disutility values at which supplemental screenings become advantageous to prescribe., Comment: 37 pages, 5 figures

Published
2022
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26. Ionic Conductive and Highly‐Stable Interface for Alkali Metal Anodes

Author

Jin, Enzhong, Tantratian, Karnpiwat, Zhao, Changtai, Codirenzi, Anastasia, Goncharova, Lyudmila V, Wang, Changhong, Yang, Feipeng, Wang, Yijia, Pirayesh, Parham, Guo, Jinghua, Chen, Lei, Sun, Xueliang, and Zhao, Yang

Subjects
Engineering, Materials Engineering, Chemical Sciences, Physical Chemistry, alkali metal anodes, atomic layer deposition, interface engineering, next generation batteries, Nanoscience & Nanotechnology
Abstract

Alkali metals are regarded as the most promising candidates for advanced anode for the next-generation batteries due to their high specific capacity, low electrochemical potential, and lightweight. However, critical problems of the alkali metal anodes, especially dendrite formation and interface stabilization, remain challenging to overcome. The solid electrolyte interphase (SEI) is a key factor affecting Li and Na deposition behavior and electrochemical performances. Herein, a facile and universal approach is successfully developed to fabricate ionic conductive interfaces for Li and Na metal anodes by modified atomic layer deposition (ALD). In this process, the Li metal (or Na metal) plays the role of Li (or Na) source without any additional Li (or Na) precursor during ALD. Moreover, the key questions about the influence of ALD deposition temperature on the compositions and structure of the coatings are addressed. The optimized ionic conductive coatings have significantly improved the electrochemical performances. In addition, the electrochemical phase-field model is performed to prove that the ionic conductive coating is very effective in promoting uniform electrodeposition. This approach is universal and can be potentially applied to other different metal anodes. At the same time, it can be extended to other types of coatings or other deposition techniques.

Published
2022

30. Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey

Author

Pirayesh, Hossein and Zeng, Huacheng

Subjects
Computer Science - Cryptography and Security
Abstract

Wireless networks are a key component of the telecommunications infrastructure in our society, and wireless services become increasingly important as the applications of wireless devices have penetrated every aspect of our lives. Although wireless technologies have significantly advanced in the past decades, most wireless networks are still vulnerable to radio jamming attacks due to the openness nature of wireless channels, and the progress in the design of jamming-resistant wireless networking systems remains limited. This stagnation can be attributed to the lack of practical physical-layer wireless technologies that can efficiently decode data packets in the presence of jamming attacks. This article surveys existing jamming attacks and anti-jamming strategies in wireless local area networks (WLANs), cellular networks, cognitive radio networks (CRNs), ZigBee networks, Bluetooth networks, vehicular networks, LoRa networks, RFID networks, and GPS system, with the objective of offering a comprehensive knowledge landscape of existing jamming/anti-jamming strategies and stimulating more research efforts to secure wireless networks against jamming attacks. Different from prior survey papers, this article conducts a comprehensive, in-depth review on jamming and anti-jamming strategies, casting insights on the design of jamming-resilient wireless networking systems. An outlook on promising antijamming techniques is offered at the end of this article to delineate important research directions.

Published
2021

31. Cardiovascular Diseases, The Leading Cause of Death in South Khorasan Province: A two-decade study: Editorial Article

Author

Tooba Kazemi, Ali Bidokhti, Seyed Mohammad Riahi, and Zahra Pirayesh

Subjects
cardiovascular diseases, covid-19, death, life’s essential 8 cardiovascular health, Medicine, Medicine (General), R5-920
Abstract

The changes in the causes of death in South Khorasan province, Iran, during 2002-2022 were examined in the present research. According to the data available in the “Mortality Data Registery” during these 20 years, cardiovascular diseases (CVD) was the most common cause of death, and its rate has increased from 20% in 2002 to about 37.8% in 2022. Even during the COVID pandemic, CVD was the first cause of death in the province. In 2022, the most common causes of death were CVD (37.8%), respiratory diseases (15.2%), cancers (14%), and accidents (9.2%). One of the most effective factors in preventing CVD is paying attention to"Life’s essential 8 Cardiovascular Health", i.e., having normal blood glucose, blood pressure, serum lipids, body mass index, sufficient physical activity, healthy diet, sufficient night sleep, and being a non-smoker. In addition to individuals’ attention to these factors, it is necessary for healthcare system decision-makers to have systematic and continuous planning to enhance people's awareness and attitude regarding these eight factors.

Published
2023

34. Technological Insights of Interoperable Models for Integration of CAD/PLM/PDM and ERP Modules in Engineering Change Management

Author

Kadam, Onkar Bhaskar, Pirayesh, Amir, Fatahi Valilai, Omid, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Kim, Kyoung-Yun, editor, Monplaisir, Leslie, editor, and Rickli, Jeremy, editor

Published
2023
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35. Using Blockchain Technology for 3D Printing in Manufacturing of Dental Implants in Digital Dentistry

Author

Shah, Sahil Sachin, Pirayesh, Amir, Fatahi Valilai, Omid, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Kim, Kyoung-Yun, editor, Monplaisir, Leslie, editor, and Rickli, Jeremy, editor

Published
2023
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38. Investigating the Impact of Leadership Styles and Feedback Behavior on Employee Creativity Growth with the Mediating Role of Communication Mechanism (in Public Clinics of Zanjan City)

Author

Reza Pirayesh, Hamed Rastegarnia, and Fatemeh Alilou

Subjects
leadership styles, feedback behavior, communication mechanism, employee creativity growth., Public aspects of medicine, RA1-1270
Abstract

Background and Objective: Employee creativity is a key element for achieving sustainable competitive advantage in both private and public organizations. This study aims to explore the influence of leadership styles and feedback behavior on employee creativity growth, considering the mediating role of the communication mechanism among employees of public clinics in Zanjan. Materials and Methods: This applied study follows a descriptive-survey approach. Data were collected through field observations, questionnaire distribution, and library research related to the study variables. The statistical population included all employees of public clinics in Zanjan. From 209 people, 135 were randomly selected as the sample based on Cochran's formula. The questionnaires were distributed and completed by these individuals. Data collected via questionnaires were analyzed using structural equation modeling with PLS and SPSS software. Results: Feedback behavior (coefficient = 0.256), leadership styles (coefficient = 0.543), and the communication mechanism (coefficient = 0.126) significantly influenced employee creativity. Additionally, feedback behavior (coefficient = 0.267) and leadership styles (coefficient = 0.348) significantly impacted the communication mechanism. The results also indicated that the communication mechanism effectively mediated the relationship between leadership styles (coefficient = 0.044) and feedback behavior (coefficient = 0.034) on employee creativity growth. Conclusion: The findings suggest that leadership styles and feedback behavior significantly influence employee creativity growth through the mediating role of the communication mechanism. Organizational managers should leverage these factors to enhance their employees' creativity

Published
2023

39. A deep learning framework to scale linear facial measurements to actual size using horizontal visible iris diameter: a study on an Iranian population

Author

Zeynab Pirayesh, Sahel Hassanzadeh-Samani, Arash Farzan, Mohammad Hossein Rohban, Mohammad Soroush Ghorbanimehr, Hossein Mohammad-Rahimi, and Saeed Reza Motamedian

Subjects
Medicine, Science
Abstract

Abstract Digital images allow for the objective evaluation of facial appearance and abnormalities as well as treatment outcomes and stability. With the advancement of technology, manual clinical measurements can be replaced with fully automatic photographic assessments. However, obtaining millimetric measurements on photographs does not provide clinicians with their actual value due to different image magnification ratios. A deep learning tool was developed to estimate linear measurements on images with unknown magnification using the iris diameter. A framework was designed to segment the eyes’ iris and calculate the horizontal visible iris diameter (HVID) in pixels. A constant value of 12.2 mm was assigned as the HVID value in all the photographs. A vertical and a horizontal distance were measured in pixels on photographs of 94 subjects and were estimated in millimeters by calculating the magnification ratio using HVID. Manual measurement of the distances was conducted on the subjects and the actual and estimated amounts were compared using Bland–Altman analysis. The obtained error was calculated as mean absolute percentage error (MAPE) of 2.9% and 4.3% in horizontal and vertical measurements. Our study shows that due to the consistent size and narrow range of HVID values, the iris diameter can be used as a reliable scale to calibrate the magnification of the images to obtain precise measurements in further research.

Published
2023
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40. Improvement of Basal Ganglia Detectability in Brain Single Photon Emission Computerized Tomography by Wavelet Transformation in Image Processing Domain: A XCAT Phantom Study

Author

Marzie Saeidikia, Hadi Seyedarabi, Babak Mahmoudian, and Jalil Pirayesh Islamian

Subjects
Parkinson’s Disease, Single Photon Emission Computed Tomography, Simulating Medical Imaging Nuclear Detectors Monte Carlo, Wavelet Transformation, Segmentation, Medical technology, R855-855.5
Abstract

Purpose: Noise in brain Single Photon Emission Computed Tomography (SPECT) images limits an early diagnosis of Parkinson's Disease (PD). To overcome the limitation, as an image processing approach, wavelet transformation was used to denoising the images also with a segmentation method to differentiate the basal ganglia in brain SPECT. Materials and Methods: The brain scans of the human XCAT phantom through the Simulating Medical Imaging Nuclear Detectors (SIMIND) simulated SPECT system were imported to the MATLAB toolkit for image processing. The reconstructed brain images by iterative reconstruction were de-noised through 9 methods of wavelet transformation at different levels, and then six segmentation methods were applied to differentiate the caudate and putamen. The Dice coefficient, Specificity, and Sensitivity evaluation criteria were calculated based on the adaptive thresholding of the selected images from segmentation. A ground truth image was manually marked by a clinical nuclear medicine specialist. Results: The dice coefficient was obtained in a range from 0.3979 to 0.6299, as well as the specificity criterion from 0.7682 to 0.8168 and the sensitivity from 0.9049 to 0.9871. The results from adaptive threshold segmentation and the evaluation criteria showed that the best levels of the nucleuses detectability were provided by level 7 of Biorthogonal, levels 4 and 7 of Coiflet, level 6 of Daubechies, level 5 of Haar, level 6 of Morlet and level 6 of Symlet methods. Conclusion: Parkinson’s disease may be diagnosed in the early stage by an image processing approach to improve the quality of brain SPECT images.

Published
2024
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42. Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities

Author

Cruz, Jorge Alberto Diaz, Biedron, Sandra, Martinez-Ramon, Manel, Sosa, Salvador, and Pirayesh, Reza

Subjects
Physics - Accelerator Physics
Abstract

Superconducting Radio-Frequency (SRF) cavities operating in continuous wave (CW) mode and with low beam loading are devices characterized by a high loaded quality factor, in the order of 10^7, and narrow bandwidth, in the order of 10 Hz. The Low Level RF (LLRF) and resonance control systems of SRF cavities become a fundamental component of the entire system operation and in general has very tight stability requirements on the amplitude, phase and resonance frequency of the cavity. Microphonics plays an important role in cavity detuning, which results in more power needed to achieve the desired gradient. Active control approaches to reduce detuning in SRF cavities using piezoelectric actuators have shown promising results. Furthermore, Machine Learning (ML) techniques have also shown important capabilities to improve existing PID controllers. Specifically, Neural Networks (NN) can be used to find optimal PID gains and improve performance of traditional control systems. In this research, we develop new approaches to improve existing LLRF and resonance control systems with ML as a tool to find the optimal gains. We investigate these approaches using the LLRF control system intended for LCLS-II., Comment: Talk and Poster presented at LLRF Workshop 2019 (LLRF2019, arXiv:1909.06754)

Published
2019

43. A Practical Spectrum Sharing Scheme for Cognitive Radio Networks: Design and Experiments

Author

Sangdeh, Pedram Kheirkhah, Pirayesh, Hossein, Quadri, Adnan, and Zeng, Huacheng

Subjects
Computer Science - Networking and Internet Architecture, Electrical Engineering and Systems Science - Signal Processing
Abstract

Spectrum shortage is a fundamental problem in wireless networks and this problem becomes increasingly acute with the rapid proliferation of wireless devices. To address this problem, spectrum sharing in the context of cognitive radio networks (CRNs) has been considered a promising solution. In this paper, we propose a practical spectrum sharing scheme for a small CRN that comprises a pair of primary users and a pair of secondary users by leveraging the multiple-input and multiple-output (MIMO) technology. In our scheme, we assume that the secondary users take full responsibility for cross-network interference cancellation (IC). We also assume that the secondary users have no knowledge about the primary network, including its signal waveform, frame structure, and network protocol. The key components of our proposed scheme are two MIMO-based interference management techniques: blind beamforming (BBF) and blind interference cancellation (BIC). We have built a prototype of our scheme on a wireless testbed and demonstrated that the prototyped secondary network can coexist with commercial Wi-Fi devices (primary users). Experimental results further show that, for a secondary device with two or three antennas, BBF and BIC achieve an average of 25dB and 33dB IC capability in an office environment, respectively.

Published
2019

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