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13th IEEE Regional Symposium on Micro and Nanoelectronics, RSM 2021

作者： Hani Zailani, Nur Fazeera Saidin, Norazlina Mohd Rusdi, Muhammad Farid Harun, Sulaiman Wadi Thirunavakkarasu, Punithavathi M. International Islamic University Malaysia Electrical and Computer Engineering Department Selangor Malaysia University of Malaya Faculty of Engineering Photonics Engineering Laboratory Department of Electrical and Engineering Kuala Lumpur Malaysia Communication Technology Section Universiti Kuala Lumpur British Malaysian Institute Kuala Lumpur Malaysia

ISBN: (纸本)9781665412315

Fiber based optical sensors have been widely used in many industries today to detect parameters such as temperature, pressure, vibrations, concentrations and many more. A Mach-Zehnder Interferometer (MZI) is well known for its compact structure and small size which are advantageous for sensing purposes. In this project, an MZI with a singlemode-multimode-singlemode-multimode (SMSM) structure is developed for measuring acetone concentration in reverse osmosis (RO) water. The MZI consists of two single mode fibers (SMF) and two multimode fibers (MMF) spliced together using an arc fusion splicer machine to produce a SMSM structure. The MZI generates a good, reflected interference spectrum where the dip wavelength is red-shifted with the increase of acetone concentration. The sensitivity of the fiber sensor is 1.5391 nm/M within the range of 0M until 4M of acetone concentration. Based on the result, the sensor has a linear response towards the changes of concentration of 94.38%. This preliminary result shows that the proposed sensor can be used to detect acetone concentration to avoid contaminated water that may be of potential concern for human health. © 2021 IEEE.

关键词： Multimode fibers

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Development and Sensitivity Analysis of Mwcnts Coated D-Shaped Plastic Optical Fiber Sensor for the Detection of Mercury

SSRN

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SSRN 2024年

作者： Khan, Abdul Ali Thirunavakkarasu, Punithavathi M. Noor, Ahmad Shukri Muhammad Saidin, Norazlina Bte Waqas, Nawaf Communication Technology Section Universiti Kuala Lumpur British Malaysian Institute Bt. 8 Jalan Sungai Pusu Selangor Gombak53100 Malaysia Faculty of I.C.T BUITEMS Quetta87100 Pakistan Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Selangor Malaysia Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia

Heavy metal pollutants in rivers and water catchments are one of the problems faced by many developing countries as they may result in health issues among their people. A proper sensing mechanism is required to ensure the quality of water. In this research, we propose a simple, low cost, and highly sensitive D-shaped plastic optical fiber (POF) sensor layered with functionalized multi-walled carbon nanotubes (MWCNTs) for the detection of mercury in water. The sensor underwent interrogation across a range of mercury concentrations at room temperature. Numerous methods of characterization have been employed to explore the intricate structural attributes of the sensing layer to verify that the nanocomposite surface layer is functionalized with minimal disruption to the nanotubes. The experimental results revealed that the coated sensor displayed swift response and recovery time of 6.3 s and 13 s respectively when tested with the dynamic mercury concentration range (0 to 5 ppm). Moreover, it exhibited a relative absorbance of 7.33%/ppm, which is approximately four times higher than the plain sensor and a limit of detection of 0.1 ppm. The sensor has the potential to be transformed into an on-site, portable, real-time optical sensor for the detection of mercury ions in drinking water, offering high sensitivity and fast data acquisition. © 2024, The Authors. All rights reserved.

关键词： Potable water

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Wide-Band and Wide-Angle Scanning Phased Array Antenna for Mobile Communication System

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 203-212页

作者： Yang, Guangwei Zhang, Yiming Zhang, Shuai Department of Electronic Systems Antennas Propagation and Millimeter-Wave Systems Section Aalborg University Aalborg9220 Denmark School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom

A wide-band phased array antenna with wide-angle scanning capability for mobile communication system is proposed in this article. An air cavity is properly embedded into the substrate under each array element. This method is very simple and can efficiently enhance wide-angle scanning performance by improving the wide-angle scanning impedance matching (WAIM) and realizing the beam-width of elements in the array. Besides, the operating bandwidth is also extended with the proposed approach. The wide-angle scanning capability is analyzed and verified by both the linear array antennas (on large ground planes in detail) and the planar array. Two 1\times 8 linear arrays and one 8\times 8 planar array are demonstrated, which achieve the beam scanning of around ±60° with a realized gain reduction under 3.5 dB in the wide operating bandwidth (37.4%). Furthermore, the beam in the E-plane can scan over ±70° with a realized gain reduction under less than 3 dB. Two linear array prototypes with wide-angle scanning capacity in two planes are fabricated and characterized, yielding good performance within overall operational bandwidth. The measured results align very well with the simulated. The proposed wideband phased array with large scanning coverage is a promising candidate for 5G mobile communications. © 2020 IEEE.

关键词： Beamforming

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Tracking user trust and mental states during cyber-attacks: A survey of existing methods and future research directions on AI-enabled decision-making for the Royal Canadian Navy 4

Tracking user trust and mental states during cyber-attacks: ...

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4th IEEE International Conference on Human-Machine Systems, ICHMS 2024

作者： Arrabito, Robert Hou, Ming Fischmeister, Sebastian Falk, Tiago H. Willoughby, Hannah Cameron, Madison Foley, Liam Normandin, Sarah Banbury, Simon Human Effectiveness Section Defence Research And Development Canada Toronto Canada University Of Waterloo Department Of Electrical And Computer Engineering Waterloo Canada Centre Emt Institut National De La Recherche Scientifique Montreal Canada C3 Human Factors St. John's Canada C3 Human Factors Toronto Canada C3 Human Factors Québec City Canada

ISBN: (纸本)9798350315790

The increased use of artificial intelligence to detect cyber-attacks against military organizations can put the operator at risk of having miscalibrated trust in the autonomous system. The present paper reviews techniques to capture the human experience of cyber-attacks to support advanced cybersecurity training strategies for the Royal Canadian Navy. Psychophysiological sensors can provide objective measures to help predict the changing mental states of cyber operators (e.g., predict cognitive load, trust, stress) of whether the operator is aware of ongoing human-noticeable aspects of cyber-attacks. Techniques for measuring the experience of cyber-attacks will help advance research on decision-making in automated systems. © 2024 IEEE.

关键词： Decision making

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Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms

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Micromachines 2023年第9期14卷 1677-1677页

作者： Zameer, Aneela Naz, Sidra Raja, Muhammad Asif Zahoor Hafeez, Jehanzaib Ali, Nasir Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Multilayer piezocomposite transducers are widely used in many applications where broad bandwidth is required for tracking and detection purposes. However, it is difficult to operate these multilayer transducers efficiently under frequencies of 100 kHz. Therefore, this work presents the modeling and optimization of a five-layer piezocomposite transducer with ten variables of nonuniform layer thicknesses and different volume fractions by exploiting the strength of the genetic algorithm (GA) with a one-dimensional model (ODM). The ODM executes matrix manipulation by resolving wave equations and produces mechanical output in the form of pressure and electrical impedance. The product of gain and bandwidth is the required function to be maximized in this multi-objective and multivariate optimization problem, which is a challenging task having ten variables. Converting it into the minimization problem, the reciprocal of the gain-bandwidth product is considered. The total thickness is adjusted to keep the central frequency at approximately 50–60 kHz. Piezocomposite transducers with three active materials, PZT5h, PZT4d, PMN-PT, and CY1301 polymer, as passive materials were designed, simulated, and statistically evaluated. The results show significant improvement in gain bandwidth compared to previous existing techniques. © 2023 by the authors.

关键词： Genetic algorithms

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Performance Analysis of Machine Learning Algorithm for the Credit Risk Analysis in the Banking Sector

Performance Analysis of Machine Learning Algorithm for the C...

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： Sandeep Kumar Hegde Rajalaxmi Hegde Krishna Priya R Sree Southry S A.V.G.A. Marthanda K. Logu Department of Computer Science and Engineering NMAM Institute of Technology Nitte (Deemed to be University) Karkala Taluk Udupi District India Department of Engineering Electrical Section University of Technology and Applied Sciences Shinas campus Oman Department of ECE Sona College of Technology Salem India Department of EEE LakiReddy BaliReddy college of Engineering Mylavaram Permanently affiliated to JNTU Kakinada Andhrapradesh India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

The banking sector has advanced in recent years. Thus, there is an increase in the demand for bank loans. The bank must distribute and sell each of the limited number of available slots to a select group of people. As a result, a usual stage is to identify who will be unable to return the loan and who will prove to be a more trustworthy option to the bank. In order to save the bank time and costs, in the proposed paper machine learning based approach is introduced to reduce the risk involved with finding the safe individual. In order to decide whether or not to grant someone a loan, this paper presents a method of loan approval based on predetermined criteria. The machine learning model for credit approval was implemented using logistic regression, XG Boost, random forest and naïve bayes model. The experimental results indicates that logistic regression model is more accurate for the credit risk analysis.

关键词： Analytical models Machine learning algorithms Costs Computational modeling Banking Performance analysis Risk analysis

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Chaotic Complex Hashing: A simple chaotic keyed hash function based on complex quadratic map

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Chaos, Solitons and Fractals 2023年第1期173卷

作者： Ayubi, Peyman Setayeshi, Saeed Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Medical Radiation Engineering Amirkabir University of Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin 64002 Taiwan

In the last two decades, the use of chaotic iterative maps in cryptographic systems, especially chaotic hash functions, has been expanding. Sensitivity to small changes in initial conditions and control parameters in chaotic systems is the best feature for designing a chaotic hash function. This paper introduces a new generalized chaotic map based on a complex quadratic map, which in addition to the chaotic behavior in the real and imaginary parts, also has a high key length, which can guarantee the security of a cryptographic system. Various analyses of dynamical systems, such as the bifurcation diagram and the Lyapunov exponent, show that the proposed chaotic map has the characteristics of a chaotic dynamical system. The foundation of a secure and efficient algorithm for designing and developing a new hash function lies in how to use this new chaotic map. Also, statistical analysis, collision analysis, key space analysis, and speed analysis use to prove the security and efficiency of the proposed method. The results of these analyses and their comparison with similar methods show that the proposed method can be a reliable and efficient method for practical applications in information technology. © 2023 Elsevier Ltd

关键词： Hash functions

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Rsr-Ids: A Reliable Score-Based Routing Protocol Using an Intrusion Detection System in Vehicular Ad-Hoc Networks

SSRN

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SSRN 2023年

作者： Banafshehvaragh, Samira Tahajomi Zarei, Mani Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Shahr-e-Qods Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan

One of the most significant challenges of vehicular ad-hoc networks (VANETs) is to establish reliable connections with the network infrastructure despite the participation concern of attacking vehicles in routing. Vehicles need a proper defense mechanism against all types of attacks even if they are accompanied by a reliable path planning strategy. This paper proposes RSR-IDS, a reliable score-based routing protocol using an intrusion detection system in VANETs. RSR-IDS detects abnormal data to calculate the untrust score (US) by deploying an intrusion detection system (IDS) in the data center. An IDS-based scoring mechanism is deployed in the data center. The classifier of the IDS is trained using three machine learning-based algorithms. These algorithms include the decision tree, random forest, and extra trees. Then, RSR-IDS selects a route with the lowest total USs and hop count compared to others for communications between the source and the destination. RSR-IDS is evaluated based on the accuracy, F1-score, false negative rate (FNR), packet delivery ratio (PDR), packet loss ratio (PLR), and end-to-end (E2E) delay criteria using OMNeT++ simulator. The most significant improvement of RSR-IDS includes 14.1% in accuracy, 11.4% in F1-score, and 5.4% in PDR regarding various vehicle densities. © 2023, The Authors. All rights reserved.

关键词： Vehicular ad hoc networks

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Modeling PROTAC Degradation Activity with Machine Learning

arXiv

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arXiv 2024年

作者： Ribes, Stefano Nittinger, Eva Tyrchan, Christian Mercado, Rocío Department of Computer Science and Engineering Section for Data Science and AI Chalmers University of Technology Chalmersplatsen 4 Gothenburg412 96 Sweden BioPharmaceuticals R&D AstraZeneca Pepparedsleden 1 Mölndal431 83 Sweden

PROTACs are a promising therapeutic modality that harnesses the cell’s built-in degradation machinery to degrade specific proteins. Despite their potential, developing new PROTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC molecules. The curated dataset incorporates important information such as pDC50 , Dmax , E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PROTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 ROC AUC, and a test accuracy of 62.3% and 0.604 ROC AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches. © 2024, CC BY.

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Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification

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computers in Biology and Medicine 2025年 185卷 109593-109593页

作者： Khan, Zeshan Aslam Waqar, Muhammad Raja, Muhammad Junaid Ali Asif Chaudhary, Naveed Ishtiaq Khan, Abeer Tahir Mehmood Anwar Raja, Muhammad Asif Zahoor International Graduate Institute of Artificial Intelligence National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Future Technology Research Center National Yunlin University of Science and Technology Section 3 123 University Road Douliou Yunlin64002 Taiwan Rawal Institute of Health Sciences Islamabad Pakistan

Over the past few decades, machine learning and deep learning (DL) have incredibly influenced a broader range of scientific disciplines. DL-based strategies have displayed superior performance in image processing compared to conventional standard methods, especially in healthcare settings. Among the biggest threats to global public health is the fast spread of malaria. The plasmodium falciparum infection, the disease origin causes the intestinal illness. Fortunately, advances in artificial intelligence techniques have made it possible to use visual data sets to quickly and effectively diagnose malaria which has also proven to be cost and time effective. In literature, several DL approaches have previously been used with good precision but suffer from computational inefficiency and interpretability. Therefore, this research proposes a generalized fractional order-based explainable lightweight convolutional neural network model to overcome these limitations. The fractional order optimization algorithms have proven worth in terms of estimation accuracy and convergence speed for different applications. The proposed fractional order optimizer-based model offers an improved solution to malaria disease diagnosis with a percentage accuracy of 95 % using the standard NIH dataset and outperforms the existing complex models concerning speed and effectiveness. The proposed fractionally optimized lightweight CNN model has shown substantial performance on the external MP-IDB dataset and M5 test set as well by achieving a generalized test accuracy of 92 % and 90.4 % which verifies the robustness and generalizability of the proposed solution under available circumstances. Moreover, the efficacy of the proposed lightweight architecture is endorsed through evaluation metrics of precision, recall, and F1-score. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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