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Advanced Technique for EEG and HRV Measurement Using Around-Ear Capacitive Electrodes

Advanced Technique for EEG and HRV Measurement Using Around-...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Usman Qamar Shaikh Anubha Manju Kalra Andrew Lowe Imran Khan Niazi Electrical and Electronic Engineering Department Faculty of Engineering Computer and Mathematical Sciences Institute of Biomedical Technologies Auckland University of Technology Auckland New Zealand Centre for Chiropractic Research New Zealand College of Chiropractic Auckland New Zealand Department of Health Science and Technology Aalborg University Aalborg Denmark

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

Long-term electroencephalogram (EEG) monitoring requires devices that are unobtrusive, comfortable, and user-friendly. Capacitive electrodes, unlike gel or dry electrodes, do not require direct galvanic skin contact, making them ideal for extended use. This paper investigates the efficacy of capacitive electrodes for recording four biopotential channels from around-ear areas. A printed circuit board was developed and assessed through an alpha-block experiment. The posterior-ear capacitive EEG achieved a statistically significant power gain (APSD) of 2.77 dB in the alpha band and 5.84 dB at 10 Hz. Additionally, a mean absolute error (MAE) of 8.9 milliseconds was observed between heart rate variability (HRV) measurements from a capacitive superior-ear electrode and shoulder ECG. These results indicate that around-ear capacitive electrodes are suitable for multimodal biopotential applications, offering a promising solution for long-term EEG monitoring.

关键词： Electrodes Measurement uncertainty Printed circuits Electrocardiography Electroencephalography Skin Recording Heart rate variability Monitoring Gain

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The Impact of Gender on Female engineering Students

The Impact of Gender on Female Engineering Students

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IEEE International Conference on Teaching, Assessment and Learning for engineering (TALE)

作者： Fesal Toosy Zarqa Mehmood Maria Talib Hafsa Siddiqi Christothea Herodotou Stefan Poslad Khurram Hamid Faculty of Engineering University of Central Punjab Lahore Pakistan Faculty of Engineering & Technology Superior University Lahore Pakistan Institute of Educational Technology Open University Milton Keynes United Kingdom School of Electronic Engineering & Computer Science Queen Mary University of London London United Kingdom Faculty of Management Sciences University of Central Punjab Lahore Pakistan

This Full - Research paper presents the results and analysis of a study conducted at three different universities in South Asia with the purpose of shedding some light on the gender-based disparities faced by female engineering students. STEM, a male dominated field, typically has less than 10% female enrolment in universities. Such a gender disbalance can have a significant effect on both the culture and work dynamics of the profession during and after student life. Studies have been conducted in different parts of the world related to the effects of this gender ratio in engineering. Yet, little research has been done on how this phenomenon affects students in South Asia. In this study, qualitative data was collected from thirty-five (N=35) female engineering students from three different universities. This was done with a semi-structured interview methodology consisting mainly of questions relating to interaction with peers, teachers and university staff. Participating students were also questioned about what kind of support they got from their family and about their awareness of the laws against harassment of women. Data were analyzed using phenomenological research techniques and eventually clustered into six themes. The first theme was ‘Pre-conceived Notions’ that the society in general and men in particular had about young women, and those notions that men (teachers and peers) had about themselves. The second theme ‘Social Relationships’, referred to connections female students had with their peers, teachers and university staff. The third theme was ‘Harassment’. The fourth theme was about ‘Hesitation’ to come forward and complain about inappropriate comments made by their teachers or peers, mostly for fear of retaliation or being singled out. The fifth theme was about awareness of the laws against harassment and related legal rights. The sixth theme was about ‘Support from Family’, including immediate and extended family. Though the issues unveiled in this st

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Impact of Varying Laser Power in WDM Transmission Systems to Control Nonlinear Effects

Impact of Varying Laser Power in WDM Transmission Systems to...

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Communication engineering and Emerging Technologies (ICoCET), IEEE International Conference on

作者： Dalal Abdulmohsin Hammood Basim Galeb Aqeel Al-Hilali Mustafa Bashar Siti Julia Rosli Cybersecurity Technology Engineering Department Electrical Engineering Technical College Middle Technical University Baghdad Iraq Computer Technician Engineering Al Hikma University College Baghdad Iraq Medical Instrumentation Engineering Al-farahidi university Baghdad Iraq Uruk University Baghdad Iraq Faculty of Electronic Engineering Technology Universiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia

ISBN: (数字)9798331504144

ISBN: (纸本)9798331504151

This study investigates how varied CW laser power broadcasts affect a sixteen-channel DWDM system. Constant transmission distance is sixty kilometers. The system can handle 0.649 Tbps. Launch power ranging from 0 to 10 dBm is studied to observe its effects on signal quality, crosstalk, and system efficiency. The QF assessment data shows that greater power levels boost optical signal-to-noise ratio (OSNR) and Q-factor. This improvement occurs at lower power levels. Compared to ambient noise, the signal is stronger. Due to increasing OSNR, Q-factor improves with power. When nonlinear effects become evident, progress may stagnate. Increased power levels may increase nonlinear processes including stimulated Brillouin scattering (SBS), four-wave mixing (FWM), and self-phase modulation (SPM). The Q-factor may decrease due to signal distortion and noise. According to the Bit Error Rate (BER) parameter, greater power increases signal intensity, which improves OSNR and reduces mistakes. Thus, BER drops. To increase an optical communication system's performance, laser power must be balanced to maximize the Q-factor, minimize the bit error rate (BER), and prevent substantial nonlinear aberrations.

关键词： Q-factor System performance Bit error rate Power lasers Nonlinear distortion Optical distortion Wavelength division multiplexing Optical noise Optical transmitters Signal to noise ratio

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Delay-Beat Differential Phase Demodulation for Laser Phase Noise Immunity in Phase-Sensitive OTDR

Delay-Beat Differential Phase Demodulation for Laser Phase N...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Heng Qian Chuan Li Chengli Li Yunnan Key Laboratory of Computer Technologies Application Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Opo-electronic Information Technology School of Physics and Electronic Information Yunnan Normal University Kunming China

A phase demodulation method immune to laser phase noise based on time-delay beat-frequency coherent Φ-OTDR is proposed. In this method, the two Rayleigh backscattered light beams with time delay are beat with local light beams respectively, and the phase difference of the two beat signals is used instead of the traditional distance-difference phase to obtain the local disturbance information. In this way, the impact of laser phase noise introduce by the distance-difference phase is avoid, and the constraint of laser linewidth on phase demodulation performance is weakened. In the experiment, a demodulation accuracy of -63.5dB rad 2 /Hz is achieved under the condition of a laser with a linewidth of 100kHz, which is improve by 11dB compared to traditional phase demodulation method.

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Investigating novel machine learning based intrusion detection models for NSL-KDD data sets

Investigating novel machine learning based intrusion detecti...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Muhammad Huzaifa Shah Muhammad Abu Bakar Raja Hashim Ali Zain Ul Abideen Usama Arshad Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

This study investigates the application of the Mutual Information (MI) feature selection technique to improve the accuracy of Machine Learning (ML) models on NSL-KDD datasets, building upon prior research. Six ML models, namely Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB), and Support Vector Machine (SVM) with different kernels (1st, 2nd, and 3rd), are implemented for classification purposes. The proposed DT model in this study shows higher accuracy than the DT model proposed in the original paper by Ingre et al. for Intrusion Detection System (IDS). Additionally, a multi-class classification model for NSL-KDD datasets is developed, considering both normalized and non-normalized features. Interestingly, it is observed that the models trained without normalized features achieve higher accuracies compared to those trained with normalized features. Moreover, the study enhances the classification performance of the DT-based IDS using the Correlation based Feature Selection (CFS) technique for feature selection. The proposed IDS is evaluated both before and after feature selection for multi-class classification (normal and various attack types) and binary classification (normal and abnormal data).

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Prediction of Polycystic Ovary Syndrome Using Genetic Algorithm-driven Feature Selection

Prediction of Polycystic Ovary Syndrome Using Genetic Algori...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Fatima Faridoon Raja Hashim Ali Zain Ul Abideen Nazia Shahzadi Ali Zeeshan Ijaz Usama Arshad Nisar Ali Muhammad Imad Said Nabi Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany BCMaterials Basque Center for Materials Applications & Nanostructure Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

PolyCystic Ovary Syndrome (PCOS) is a hormonal disorder frequently found in women of reproductive age having a significant impact on the cause of infertility. It is an endocrine condition characterized by abnormalities in female hormone levels and aberrant synthesis of male hormones. This syndrome causes ovarian malfunction, increasing the risk of miscarriage and infertility. PCOS has a wide range of symptoms, making a diagnosis difficult. In this study, we proposed a feature selection method based on genetic algorithm with logistic regression to increase the accuracy of early diagnosis. A dataset containing the clinical and biochemical characteristics of 109 patients, including 36 with PCOS, is used. The genetic algorithm is used to extract the most important features from the dataset. Then, a logistic regression model is used for classification. The proposed model outperformed the baseline model that used all features, which had an accuracy of 86.2%, and produced a markedly improved accuracy of 95.4%. These results show that the proposed model effectively locates key characteristics for PCOS diagnosis.

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Deep Learning Techniques in Mobile Edge Computing for Internet of Medical Things

Deep Learning Techniques in Mobile Edge Computing for Intern...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Abdulwadood Alawadhi R. Badlishah Ahmad Abdullah Almogahed Ahmad Abrar Department of Computers and Information Technology Faculty of Engineering and Computing) Yemen Faculty of Electronic Engineering and Technology Universiti Malaysia Perlis Perlis Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok-Kedah Malaysia

The fast expansion of the Internet of Medical Things (IoMT) has resulted in a ubiquitous home health diagnostic network. High patient demand results in high costs, short latency, and communication overload. As a result, 6G is the next generation of IoT, IoMT, and cellular networks, intending to considerably improve the quality of smart healthcare services through high throughput and decreased latency. So far, adopting cloud computing for time-critical applications and decreasing access delays to resources is difficult. Deep learning has been extensively employed to extract characteristics from complicated networks as artificial intelligence technology has advanced. On the other hand, deep learning models are often run in cloud computing data centres with tremendous processing resources. Conventional cloud computing systems primarily rely on the network, which has significant latency and security and privacy issues. One of the successful solutions is the Mobile Edge Computing (MEC) paradigm, which brings cloud computing services closer to the edge network and uses available resources. Mobile edge computing places computer and storage nodes near mobile devices at the Internet's edge, leading to considerable savings in system operating time, memory cost, and power usage. Deep learning is used in mobile edge computing to forecast changes in demand based on daily patient behaviours. It also prepares the network by scaling up network resources as required. This study aims to discuss IoMT and identfy the problems in deep learning for mobile edge computing technology and its applications.

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Wav2Vn: Scalable Unsupervised Pre-training and Adaptation for Multi-Accent Vietnamese ASR

Wav2Vn: Scalable Unsupervised Pre-training and Adaptation fo...

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Green Buildings (ATiGB), Applying New technology in

作者： Viet Nguyen Kim Huyen Nguyen Thi Ngoc Hieu Dam Minh Nguyen Do Hoang Khoi Tien-Dung Do Cao Truong Dung Department of Information Technology Posts and Telecommunication Institute of Technology Ha Noi Vietnam Department of Computer Science Swinburne University Ha Noi Vietnam Department of Computer Science Posts and Telecommunication Institute of Technology Ha Noi Vietnam Faculty of Information Posts and Telecommunication Institute of Technology Ha Noi Vietnam Department of Electronic Engineering Posts and Telecommunication Institute of Technology Ha Noi Vietnam

ISBN: (数字)9798331505042

ISBN: (纸本)9798331505059

Automatic speech recognition (ASR) systems have seen significant advancements in recent years, driven by deep learning and large-scale pre-training methods. However, achieving high accuracy across multiple accents remains a challenging task, especially for languages with rich phonetic diversity like Vietnamese. The Vietnamese language encompasses a wide range of regional accents, each with unique phonetic characteristics that can hinder the performance of conventional ASR. To address this issue, we present a Vietnamese language processing model called Wav2Vn as a novel approach to scalable unsupervised pretraining and adaptation for ASR targeting multiple Vietnamese accents. Our method leverages the power of the Mixture of Experts (MoE) to effectively handle the diverse phonetic variations across different Vietnamese accents. Our results demonstrate a significant performance improvement in terms of word error rate (WER) and robustness to accent variability compared to the current state-of-the-art PhoWhisper.

关键词： Adaptation models Error analysis Green buildings Scalability Diversity reception Self-supervised learning Phonetics Robustness Computational efficiency Automatic speech recognition

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Enhanced Dual-Active Bridge Converter with Inherent Bipolar Operation Capability for LVDC Distribution Systems 1

Enhanced Dual-Active Bridge Converter with Inherent Bipolar ...

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1st IEEE International Power electronics and Application Symposium, PEAS 2021

作者： Cui, Shenghui Hu, Jingxin Zhao, Shanshan Seoul National University Department of Electrical And Computer Engineering Seoul Korea Republic of RWTH Aachen University Faculty of Electrical Engineering And Information Technology Aachen Germany Huazhong University of Science And Technology School of Electrical And Electronic Engineering Wuhan China

ISBN: (纸本)9781665413596

Low-voltage direct-current (LVDC) distribution is an efficient way to accommodate high penetration of distributed energy generations, energy storage systems, and electronic loads in the distribution systems. This paper presents a topology of dual-active bridge (DAB) converter for bipolar LVDC distribution systems. By employing a dedicated winding connection in the ac-link transformer, the operation of two dc poles on the input side can be fully decoupled. Hence, the DAB can provide the full bipolar-operation capability without using additional active or passive components. In case one pole of the bipolar LVDC distribution is failed due to, e.g., wire disconnection, the dc-link input current of the faulty pole on the input side of the DAB converter can be transferred to the healthy pole immediately, and sustain the power supply to the output side. The validity of the topology and the control proposed in this work is verified by both simulations and experimental results. © 2021 IEEE.

关键词： Poles

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Chinese Temple shaped slotted monopole UWB patch antenna for Microwave Imaging Applications

Chinese Temple shaped slotted monopole UWB patch antenna for...

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2021 International Conference on Information and Communication technology for Sustainable Development, ICICT4SD 2021

作者： Talukder, Md Siam Samsuzzaman, Md Hasan, Md Mahedi Islam, Md Tarikul Hossain, Ismail Singh, Mandeep Singh Jit Patuakhali Science and Technology Faculty of Computer Science and Engineering Patuakhali Bangladesh Universiti Kebangsaan Malaysia Dept of Electrical Electronic and Systems Engineering Selangor Malaysia Universiti Kebangsaan Malaysia Selangor Malaysia

ISBN: (纸本)9781665414609

A new Chinese Temple shaped (CTS) slotted monopole UWB patch antenna is introduced in this research for the purposes of microwave imaging applications such as breast imaging. Geometrically the antenna was being comprised of a slotted pentagonal radiator with a diminished slotted ground-plane carved rectangularly. On the patch and ground plane, numerous types of slots were explored. Slotted pentagonal-shape patch and slotted rectangular ground offer significant Gain over 2.4 to 7.8GHz frequency ranges, wide bandwidth, along with comparatively balanced radiation properties. Whole dimension of the prototype is 40×40×1.6mm3 with 0.320λ×0.320λ×0.0128λ electrical dimension at starting frequency 2.4GHz. The antenna has a fractional bandwidth of 105.88% and an operating bandwidth of 5.4 GHz (2.4-7.8GHz). Several parameters are taken into account and optimized to maximize Gain, and the maximum gain value is 6 dBi. Efficiency reaches a maximum of 96.23% with an average of 94.32% within 2.4-7.8 GHz frequency ranges. Being obtainable and affordable, the prototype mounted on 1.6 mm thick FR-4 substrate with such a 4.6 and 0.002 Permeability and loss tangent respectively. Simulation performed through 3D simulator software CST Microwave Studio 2019 and HFSS. May the antenna really be a respectable contender to use in MI applications for its lightweight structure and high Gain. © 2021 IEEE.

关键词： Microstrip antennas

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