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نتیجه جستجو - الکترود

تعداد مقالات یافته شده: 27
ردیف عنوان نوع
1 Plasmonic Waveguides: Enhancing quantum electrodynamic phenomena at nanoscale
موجبرهای پلاسمونیک: افزایش پدیده های الکترودینامیکی کوانتومی در مقیاس نانو-2022
The emerging field of plasmonics may lead to enhanced light–matter interactions at extremely nanoscale regions. Plasmonic (metallic) devices promise to effi- ciently control classical and quantum properties of light. Plasmonic waveguides are usually employed to excite confined electromagnetic modes at nanoscale that can strongly interact with matter. Analysis shows that nanowaveguides share similarities with their low-frequency microwave counterparts. In this article, we review ways to study plasmonic nanostructures coupled to quantum optical emitters from a classical electromagnetic perspective. Quantum emitters are mainly used to generate single-photon quantum light that can be employed as a quantum bit, or “qubit,” in envisioned quantum information technologies. We demonstrate different ways to enhance a diverse range of quantum electrodynamic phenomena based on plasmonic configurations by using the Green’s function formalism, a classical dyadic tensor. More specifically, spontaneous emission and superradiance are analyzed through Green’s function-based field quantization. The exciting new field of quantum plasmonics could lead to a plethora of novel optical devices for communications and computing applications in the quantum realm, such as efficient single-photon sources, quantum sensors, and compact on-chip nanophotonic circuits.
مقاله انگلیسی
2 Person-identification using familiar-name auditory evoked potentials from frontal EEG electrodes
شناسایی فرد با استفاده از پتانسیل نام-آشنا شنوایی الکترودهای EEG جلو برانگیخته-2021
Electroencephalograph (EEG) based biometric identification has recently gained increased attention of re- searchers. However, state-of-the-art EEG-based biometric identification techniques use large number of EEG electrodes, which poses user inconvenience and consumes longer preparation time for practical applications. This work proposes a novel EEG-based biometric identification technique using auditory evoked potentials (AEPs) acquired from two EEG electrodes. The proposed method employs single-trial familiar-name AEPs extracted from the frontal electrodes Fp1 and F7, which facilitates faster and user-convenient data acquisition. The EEG signals recorded from twenty healthy individuals during four experiment trials are used in this study. Different com- binations of well-known neural network architectures are used for feature extraction and classification. The cascaded combinations of 1D-convolutional neural networks (1D-CNN) with long short-term memory (LSTM) and with gated recurrent unit (GRU) networks gave the person identification accuracies above 99 %. 1D-convolutional, LSTM network achieves the highest person identification accuracy of 99.53 % and a half total error rate (HTER) of 0.24 % using AEP signals from the two frontal electrodes. With the AEP signals from the single electrode Fp1, the same network achieves a person identification accuracy of 96.93 %. The use of familiar-name AEPs from frontal EEG electrodes that facilitates user convenient data acquisition with shorter preparation time is the novelty of this work.
Keywords: Auditory evoked potential | Biometrics | Deep learning | Electroencephalogram | Familiar-name | Person identification
مقاله انگلیسی
3 Investigation of eye tracking, electrodermal activity and facial expressions as biometric signatures of food reward and intake in normal weight adults
بررسی ردیابی چشم ، فعالیت الکترودرمی و حالات چهره به عنوان نشانه های بیومتریک پاداش و مصرف غذا در بزرگسالان با وزن طبیعی-2021
Pervasive exposure to a vast and varied food repertoire has contributed to the obesity epidemic. Within this issue, there is a need for a better understanding of the psychophysiological responses to food cues that precede food choice and food intake to establish how these responses contribute to the link between food availability and increasing obesity levels. Biometric measures such as eye tracking, electrodermal activity and facial expressions may separately or collectively provide deeper insight into psychophysiological processes underlying food reward and food intake. We examined how biometric responses differed in foods varying in fat and taste and explored how these biometric signatures to food cues were related to food preference behaviours, food choice, and food intake. We developed and tested a biometric food preference task designed to concurrently assess biometric responses (eye tracking, electrodermal activity and facial expressions) and food reward to visual food stimuli from different food categories in 100 normal weight adults. Food intake and selection was examined using a simultaneous choice ad libitum buffet. The results from this cross-sectional study showed significant differences in visual attention towards foods varying in fat content and taste prior to making rapid food choice decisions. Furthermore, the study found positive associations between maintained attention during a forced choice paradigm and subsequent food reward and food intake measures. Attention, arousal and facial expression during passive viewing were not associated with food reward or intake measures, except for an association between negative valence and explicit liking such that less liked foods elicited stronger negative facial expressions. The findings indicate that implicit, biometric responses to food cues predict both food reward and actual food intake.
Keywords: Appetite | Food intake | Hedonic eating | Food reward | Biometrics
مقاله انگلیسی
4 Bilayer systems based on conjugated polymers for fluorescence development of latent fingerprints on stainless steel
سیستم های Bilayer مبتنی بر پلیمرهای هملند برای توسعه فلورسانس اثرانگشت نهان روی فولاد ضد زنگ-2020
Fingerprints are a key role in criminal investigations and are the most commonly used form of physical evidence for identifying criminals or to establish a connection between crime scenes. However, visualizing latent (hidden) fingerprints is steal a great challenge, mainly when they are present on metallic surfaces. In this work, a new concept based on the electrodeposition of bilayer systems based on conjugated and fluorescent polymers was used for the development of latent fingerprints on stainless steel. The first layer of Polypyrrole or PEDOY was electrodeposited onto the surface containing a latent fingerprint by oxidation of the monomer in LiClO4 aqueous solution. The second layer of a fluorescent Poly(2,2′:5′,2″-terthiophene) was electrodeposited onto the first layer by using a solution of (C4H9)4NBF4/CH3CN. Such bilayer systems showed fluorescent properties and may be applied in forensic biometry for development of latent fingerprints on stainless steel, since this strategy affords images with high definition in both visible and UV light, permitting the recognition of the ridge patterns and singular points in order to be used for confrontation with other dactyloscopy images.
Keywords: Conjugated polymers | Electrodeposition | Fluorescence | Latent fingerprints | Forensic chemistry
مقاله انگلیسی
5 A review on microelectrode recording selection of features for machine learning in deep brain stimulation surgery for Parkinson’s disease
مروری بر انتخاب ضبط میکروالکترود ویژگی های یادگیری ماشین در عمل جراحی تحریک عمیق مغز برای بیماری پارکینسون-2019
Objective: This study seeks to systematically review the selection of features and algorithms for machine learning and automation in deep brain stimulation surgery (DBS) for Parkinson’s disease. This will assist in consolidating current knowledge and accuracy levels to allow greater understanding and research to be performed in automating this process, which could lead to improved clinical outcomes. Methods: A systematic literature review search was conducted for all studies that utilized machine learning and DBS in Parkinson’s disease. Results: Ten studies were identified from 2006 utilizing machine learning in DBS surgery for Parkinson’s disease. Different combinations of both spike independent and spike dependent features have been utilized with different machine learning algorithms to attempt to delineate the subthalamic nucleus (STN) and its surrounding structures. Conclusion: The state-of-the-art algorithms achieve good accuracy and error rates with relatively short computing time, however, the currently achievable accuracy is not sufficiently robust enough for clinical practice. Moreover, further research is required for identifying subterritories of the STN. Significance: This is a comprehensive summary of current machine learning algorithms that discriminate the STN and its adjacent structures for DBS surgery in Parkinson’s disease.
Keywords: Parkinson’s disease | Machine learning | Microelectrode recording | Automation
مقاله انگلیسی
6 Data driven feature selection and machine learning to detect misplaced V1 and V2 chest electrodes when recording the 12 lead electrocardiogram
انتخاب ویژگی داده محور و یادگیری ماشین برای تشخیص الکترودهای قفسه سینه V1 و V2 در زمان ضبط الکتروکاردیوگرام 12 -2019
Background: Electrocardiogram (ECG) lead misplacement can adversely affect ECG diagnosis and subsequent clinical decisions. V1 and V2 are commonly placed superior of their correct position. The aim of the current studywas to usemachine learning approaches to detect V1 and V2 leadmisplacement to enhance ECG data quality. Method: ECGs for 453 patients, (normal n=151, Left VentricularHypertrophy (LVH) n=151,Myocardial Infarction n=151) were extracted frombody surface potentialmaps. Thesewere used to extract both the correct and incorrectly placed V1 and V2 leads. The prevalence for correct and incorrect leads were 50%. Sixteen features were extracted in three different domains: time-based, statistical and time-frequency features using a wavelet transform. A hybrid feature selection approachwas applied to select an optimal set of features. To ensure optimal model selection, five classifiers were used and compared. The aforementioned feature selection approach and classifiers were applied for V1 and V2 misplacement in three different positions: first, second and third intercostal spaces (ICS). Results: The accuracy for V1 misplacement detection was 93.9%, 89.3%, 72.8% in the first, second and third ICS respectively. In V2, the accuracywas 93.6%, 86.6% and 68.1% in the first, second and third ICS respectively. There is a noticeable decline in accuracy when detecting misplacement in the third ICS which is expected.
Keywords: Machine learning | Feature extraction | Body surface potential maps | Lead misplacement | Electrode misplacement | Chest leads
مقاله انگلیسی
7 پیش به سوی استقرار معیارهای عملکردی استاندارد برای باتری ها، ابرخازن ها و فراتر از آن
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 70 - تعداد صفحات فایل doc فارسی: 238
طی دهه ی گذشته، ادوات ذخیره ساز انرژی الکتروشیمیایی (EES) تا حد زیادی ارتقا یافته اند، و گستره ی وسیعی از مواد فعال الکترودی پیشرفته و دستگاه هایی با معماری جدید ارائه شده اند. این دستگاه ها و مواد جدید بایستی در رابطه با معیارهای دقیق و روشن، در وهله ی نخست براساس شواهدی از عملکرد واقعی خود، ارزیابی شوند. بطور معمول در متون و مقالات مجموعه ای از معیارها برای تعیین مشخصه ها و گزارش عملکرد سیستم های EES استفاده می شوند. بااینحال، از آنجاکه سیستم های پیشرفته ی EES پیچیدگی فزاینده ای می یابند، روش ارزیابی قابل اطمینان عملکرد مواد فعال الکترودی نیاز به اصلاح دارد تا توافقی واقعی به وجود بیاید و نیز محدودیت های این فناوری ها که سرعت پیشرفت بالایی دارند و حوزه های هدف برای توسعه ی آتی تحقق یابند. در نبود دسته ای متمرکز از معیارها که مقبولیت عام داشته باشند، ممکن است موارد عدم انطباق مابین مقادیر نسبت داده شده به مواد و دستگاه ها و عملکرد واقعی آنها پدید آید. ما در اینجا به بررسی ادوات ذخیره ساز انرژی از خازن های معمولی گرفته تا ابرخازن ها و سیستم های هیبرید و نهایتاً باتری ها می پردازیم. معیارها برای ارزیابی سیستم های ذخیره ی انرژی تشریح می شوند، هرچند که تمرکز بر سیستم های هیبریدی و خازنی ذخیره سازی انرژی است. افزون بر این، ما به بحث بر سر چالش هایی می پردازیم که هنوز هم نیاز است برای استقرار معیارهای پیچیده تر برای ارزیابی سیستم های EES حل و فصل شوند. امیدواریم این اقدام موجب تقویت گفتمان موجود شده و درک بیشتر از این معیارها جهت توسعه ی پروتکلی بین المللی برای ارزیابی دقیق سیستم های EES را ارتقا بخشد.
مقاله ترجمه شده
8 Assessment of muscle fatigue during isometric contraction usingautonomic nervous system correlatesAlberto
ارزیابی خستگی عضلات در هنگام انقباض ایزومتریک با استفاده از سیستم عصبی اتونومیک در ارتباط آلبرتو-2019
Muscle fatigue is a complex phenomenon that results in a reduction of the maximal voluntary force.Measuring muscle fatigue can be a challenging task that may involve the use of intramuscular electrodes(i.e., intramuscular electromyography (EMG)) or complex acquisition techniques. In this study, we pro-pose an alternative non-invasive methodology for muscle fatigue detection relying on the analysis of twoautonomic nervous system (ANS) correlates, i.e., the electrodermal activity (EDA) and heart rate variabil-ity (HRV) series. Based on standard surface EMG analysis, we divided 32 healthy subjects performingisometric biceps contraction into two groups: a fatigued group and a non-fatigued group.EDA signals were analyzed using the recently proposed cvxEDA model in order to derive phasic andtonic components and extract effective features to study ANS dynamics. Furthermore, HRV series wereprocessed to derive several features defined in the time and frequency domains able to estimate the car-diovascular autonomic regulation. A statistical comparison between the fatigued and the non-fatiguedgroups was performed for each ANS feature, and two EDA features, i.e., the tonic variability and the phasicresponse rate, showed significant differences. Moreover, a pattern recognition procedure was applied tothe combined EDA-HRV feature-set to automatically discern between fatigued and non-fatigued sub-jects. The proposed SVM classifier, following a recursive feature elimination stage, exhibited a maximalbalanced accuracy of 83.33%. Our results demonstrate that muscle fatigue could be identified in a non-invasive fashion through effective EDA and HRV processing.
Keywords:Muscle fatigue | Autonomic nervous system | Heart rate variability | Electrodermal activity | Pattern recognition
مقاله انگلیسی
9 Application of the voltammetric electronic tongue based on nanocomposite modified electrodes for identifying rice wines of different geographical origins
کاربرد زبان الکترونیکی ولتامتری بر اساس الکترودهای اصلاح شده نانو کامپوزیت برای شناسایی شراب برنج با منشأ جغرافیایی مختلف-2019
In the study, the voltammetric electronic tongue based on three nanocomposites modified electrodes was applied for the identification of rice wines of different geographical origins. The nanocomposites were prepared by gold and copper nanoparticles in the presence of conducting polymers (polymer sulfanilic acid, polymer glutamic acid) and carboxylic multi - walled carbon nanotubes. The modified electrodes showed high sensitivity to guanosine - 5 - monophosphate disodium salt, tyrosine and gallic acid which have good correlation with the geographical origins of rice wines. Scanning electron microscopy was performed to display the surface morphologies of the nanocomposites, and cyclic voltammetry was applied to study the electrochemical behaviors of the taste substances on the electrode surfaces. Four types of electrochemical parameters (pH, scan rates, accumulation potentials and time) were optimized for getting a low limit of the detection of each taste substance. The geographical information of rice wines was obtained by the modified electrodes based on two types of multi - frequency large amplitude pulse voltammetry, and “area method” was applied for extracting the feature data from the original information obtained. Based on the area feature data, principal component analysis, locality preserving projection (LPP), and linear discriminant analysis were applied for the classification of the rice wines of different geographical origins, and LPP presented the best results; extreme learning machine (ELM) and alibrary for support vector machines were applied for predicting the geographical origins of rice wines, and ELM performed better.
Keywords: Nanocomposites modified electrodes | Conducting polymer | Multi - walled carbon nanotubes | Rice wine | Pattern recognition
مقاله انگلیسی
10 زبان الکترونیکی ولتامتری صفحه چاپی برای آنالیز مخلوط های پیچیده یون های فلزی
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 36 - تعداد صفحات فایل doc فارسی: 28
زبان الکترونیکی ولتامتری از چهار الکترود اصلاح شده ی صفحه چاپی تشکیل شده است: یک الکترود اصلاح-شده ی نانوالیاف کربنی، یک الکترود غشای آنتیموانی خارج از محل تهیه شده از الکترود اصلاح شده ی نانوالیاف کربنی، و دو الکترود اصلاح شده ی کربنی که به لحاظ شیمایی با Cys و GSH اصلاح شده اند. این زبان با موفقیت در آنالیز مخلوط پیچیده ی یون های فلزی (4 آنالیت [ماده ی اصلی مورد بررسی] و 2 تداخلی) با ولتامتری عاری سازی آندی پالس تفاضلی مورد استفاده قرار گرفت. تمامی حسگرها نخست مورد بررسی قرار گرفتند تا شناسایی تمامی فلزات بطور جداگانه تأیید کند که تمامی الکترودها پاسخ های متمایزی برای فلزات نتیجه می دهند. سیگنال های ولتامتری ارائه شده توسط آرایه ی حسگرها توسط رگرسیون حداقل مربعات جزئی (PLS) ارزیابی شدند تا ماهیت تداخلی اندازه گیری های عاری سازی چندفلزی حاصله، از بین برود. این مدل PLS با در نظر گرفتن یک مدل سلسله مراتبی به منظور کاستن از حجم عظیم داده ها ایجاد شد. این روش برای مخلوط های ساختگی Cd(II)، Pb(II)، Tl(I)، و Bi(III) در حضور Zn(II) و In(III) با میزان میکروگرم بر لیتر استفاده شد و با موفقیت با ضرایب همبستگی کالیبراسیون و پیش بینی بالاتر از 0.9 حاصل از نمودارهای "غلظت پیش بینی شده بر حسب غلظت مورد انتظار" اعتبارسنجی شد. افزون بر این، شناسایی همزمان Cd(II)، Pb(II)، Tl(I)، و Bi(III) در حضور Zn(II) و In(III) در آب غنی شده نیز بطور رضایت بخش به دست آمد و نتایجی قابل مقایسه با نتایج حاصل از ICP-MS به دست آمد.
کلمات کلیدی: زبان الکترونیکی ولتاژسنجی | الکترودهای صفحه چاپی | الکترودهای اصلاح شده | شناسایی فلز | ولتامتری عاری سازی | رگرسیون حداقل مربعات جزئی
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