FCM – Computerized Calculations vs Role of Experts

Arthur Yosef¹, Eli Shnaider² and Moti Schneider³, ¹Tel Aviv-Yaffo Academic College, Israel, ²Israel, ³Netanya Academic College, Israel

ABSTRACT

This study presents a method to assign relative weights when constructing Fuzzy Cognitive Maps (FCMs). We introduce a method of computing relative weights of directed edges based on actual past behavior (historical data) of the relevant concepts. There is also a discussion addressing the role of experts in the process of constructing FCMs. The method presented here is intuitive, and does not require any restrictive assumptions. The weights are estimated during the design stage of FCM and before the recursive simulations are performed.

KEYWORDS

FCM, relative importance (weight), Fuzzy Logic, Soft Computing, Neural Networks.

Prominent Discord Discovery with Matrix Profile : Application to Climate Data Insight

Hussein El Khansa, Carmen Gervet and Audrey Brouillet, Espace-Dev, Univ. Montpellier, IRD, Univ. Guyane, Univ. La Reunion, Montpellier, France

ABSTRACT

Outlier detection is a challenging problem in data analysis, especially regarding the definition and extraction of actionable anomalous patterns. Identifying the criteria that render a pattern more singular or insightful is of crucial importance, at the heart of our study. In this paper, we propose the concept of prominent discord. The core idea behind this new concept is to identify dependencies among discords of varying lengths. How can we identify a discord that is prominent with respect to the other ones? We propose an ordering relation, that ranks discords and we seek a set of prominent discords with respect to this ordering. Our contributions are 1) a formal definition, ordering relation and methods to derive prominent discords based on the Matrix Profile techniques, and 2) their evaluation over large contextual climate data, covering 110 years of monthly data. The approach is generic and its pertinence shown over historical climate data.

KEYWORDS

Prominent discord discovery, Large time series, Matrix profile, Climate data.

Creation Of A Monitoring System For Images Of Earth Remote Sensing Objects Using Modern Technologies

KasatikovN.N., FadeevaA.D., Moscow Aviation Institute (National Research University), Russian Federation

ABSTRACT

The article discusses the approach to processing and analysis of earth remote sensing data, which are based on space and/or satellite images, images from the Internet. The results of the solution for determining the images of objects and the operation of neural networks for automated detection are shown.

KEYWORDS

remote sensing of the Earth, convolutional neural networks, definition of object images.

An Informational Space based Semantic Analysis for Scientific Texts

Neslihan Suzen, Alexander N. Gorban, Jeremy Levesley and Evgeny M. Mirkes, School of Computing and Mathematical Sciences, University of Leicester, Leicester, UK

ABSTRACT

One major problem in Natural Language Processing is automatic analysis and representation of human language. Human language is ambiguous and deeper understanding of semantics and creating human-to-machine interaction have required an effort in creating the schemes for act of communication and building common-sense knowledge bases for the ‘meaning’ in texts. This paper introduces computational methods for semantic analysis and the quantifying the meaning of short scientific texts. Computational methods extracting semantic feature are used to analyse the relations between texts of messages and ‘representations of situations’ for a newly created large collection of scientific texts, Leicester Scientific Corpus. The representation of scientific-specific meaning is standardised by replacing the situation representations, rather than psychological properties, with the vectors of some attributes: a list of scientific subject categories that the text belongs to. First, this paper introduces ‘Meaning Space’ in which the informational representation of the meaning is extracted from the occurrence of the word in texts across the scientific categories, i.e., the meaning of a word is represented by a vector of Relative Information Gain about the subject categories. Then, the meaning space is statistically analysed for Leicester Scientific Dictionary-Core and we investigate ‘Principal Components of the Meaning’ to describe the adequate dimensions of the meaning. The research in this paper conducts the base for the geometric representation of the meaning of texts.

KEYWORDS

Natural Language Processing, Information Extraction, Scientific Corpus, Scientific Dictionary, Quantification of Meaning, Word Representation, Text Representation, Dimension Extraction, Dimensionally Reduction, Principal Component Analysis, Meaning Space.

A Wearable and Internet-Of-Things (IoT) Application for Sleep Detection and Lighting Control using AI and Machine Learning Techniques

William Ma¹, Yu Sun², ¹Crean Lutheran High School, 12500 Sand Canyon Ave, Irvine, CA 92618, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

There are many apps that let you control hardwares with the application of the internet-of-thing today, however, I have seen none that lets you customly control the hardwares, most likely you can only use the few controls the developer of the hardware gives you, and there are very few auto control options. This paper designs an application to auto control the hardwares into desired state based on personal status detected [1]. We use a phone to detect the sound of the user’s breath and determine with the sound if they are asleep, and once they are asleep, we turn of a light switch in the users room to create a total darkness environment. Much research done on sleeping quality shows that sleeping in total darkness gives much better sleeping quality, while many, out of fear or sleeping disorders, still leave little lights on when sleeping [3]. This software helps to give these people better sleeping quality with ease [2].

KEYWORDS

IOT, AI, Machine learning.

Systematic Analysis of Virtual Higher Education using ICT Convergence Technology in Tamilnadu during Covid-19

Dr. SUTHA K, SRM, India

ABSTRACT

Education is a primary part of every human life. This Covid-19 pandemic situation changes everything in an unexpected way and everyone suffered lot due to this unpredicted lockdown period. In this pandemic situation, education is also getting a question mark for every child past one and half years. Initially, online teaching and learning is not comfortable to the teachers and students due to lot of reasons such as Tower/Network problem, Desktop/Laptop/Mobile phone technical problem, Low bandwidth, Power supply problem, Wi-Fi issues and many more. This paper discusses systematic analysis of virtual higher education by ICT (Information and Communication Technology) convergence technology in Tamilnadu during Covid-19. This pandemic situation ICT convergence technology plays a vital role to connect everyone in a digital environment and uses different kind of communication technologies into a single forum called virtual classroom. For this research work, online questionnaires were created in Google Form and circulated to all over Tamil Nadu through WhatsApp, Email and Social Media platform. This online survey creates a great impact among students’ community and received massive responses from different category of students all over Tamil Nadu. Moreover, this paper surely helps the reader and researchers to understand the impact of Covid-19 on higher education in Tamil Nadu and extensive analyses were completed by various aspects of online education. Experimental results are done through SPSS tool to generate better evidence for future researchers and results are shown in real time environment. Finally, the perspective of online education among students in Tamil Nadu are concluded with pros and cons of online learning and their suggestions were included well to improve efficient ICT teaching-learning process for future generation.

KEYWORDS

Online Education, Covid – 19, Higher Education, Lockdown, Virtual Classroom, Tamil Nadu, ICT.

Assessment of Extrinsic and Intrinsic Motivational Factors among Science Teachers Attrition and Retention in Minna Metropolis Niger State

HARUNA, H. A., BASHIR A.U., & HASSAN A.A, Department of Science Education, Federal University of Technology Minna, Niger state, Nigeria

ABSTRACT

Teacher attrition and turnover is an educational wastage in terms of resources and manpower. The purpose of this study was to determine the assessment of extrinsic and intrinsic motivational factors on science teachers attrition and retention in public secondary school in Minna metropolis Niger state.. The study adopted the descriptive survey design and was guided by two research questions. The population of the study consists of 1320 science teachers in Minna metropolis, Niger state. The sample for the study was 132 science teachers in Minna metropolis Niger state. Researcher designed a 5-point scale questionnaire which was validated by experts, tested for reliability and was used for data collection. Mean and standard deviations were used to answer the two research questions. The findings show among others that teacher attrition or teacher turnover takes place for a number of reasons which include low social recognition for teachers and lack of opportunities for professional development. Based on the findings, it was recommended that government should intensify programmes for teachers’ development and capacity building. The society in general and employers of teachers in particular should do all that is needed to accord the teaching profession its rightful pride of place among professions.

KEYWORDS

Attrition, Extrinsic and Intrinsic Motivation, Teacher Retention.

Facebook Social Learning Group (FBSLG) as a Classroom Learning Management Tool

Jomar M. Urbano, Ph.D, College of Education, Nueva Ecija University of Science and Technology, Philippines

ABSTRACT

This study focuses on the step-to-step procedure in creating Facebook Social Learning Group (FBSLG) and the perception of students on using FBSLG as learning management tool. Descriptive method was employed in this study participated by two hundred eighty (280) teacher education students in Nueva Ecija University of Science and Technology – College of Education during the academic year 2020-2021 who were purposively selected based on the criteria set by the researcher. Five simple steps on creating FBSLG were discussed by the researcher. Using the adapted survey questionnaire, the data revealed that the students strongly agreed on the four aspects of perception about FBSLG namely: pedagogical affordance, social affordance, technological affordance, and their overall perception on the platform. Some worries of the students on the platform were addressed like the safety of sharing ideas in the FBSLG and some technical difficulties were encountered. It is concluded that the FBSLG is an effective and efficient way of conducting classes during this time of pandemic, so the use of this platform to other subjects is highly recommended.

KEYWORDS

Facebook Social Learning Group, Learning Management Tool, Learning Management System, Social Media.

Blockchain Technology to Help us Serve, Protect and Care for Children in their Early Years

Dr.Samia Kazi, Global Childhood Academy, United States of America

ABSTRACT

Blockchain is a novel technology that is continuously evolving and being applied in various domains. Its birth is from the well-known digital currency proposed by Satoshi Nakamoto, Bitcoin. Initially, it was only used for financial transactions, but now it is also being used or proposed in every domain that requires immutable and secure record-keeping or ledger. As part of the fourth industrial revolution since the invention of the steam engine, electricity, and information technology, blockchain technology has been applied in many areas such as finance, judiciary, and commerce. The advantages of blockchain technology in early childhood education range from data management, data authentication, protection, accreditation, credentialing, and much more, as we will see in this paper. Blockchain data is available and verifiable 24 hours a day, seven days a week, with complete accountability. Blockchain technology is commonly used in education to issue and authenticate educational credentials. Thanks to blockchain technologies, the credential process is streamlined, and employers can expend less time verifying academic performance. Blockchain also offers a secure forum for sharing education-related or child-related sensitive information, increasing confidence, lowering costs, and increasing accountability. More specifically, this article focuses on its potential educational applications for early childhood education and explores how blockchain technology can be used to solve some of the challenges faced by the sector as a whole. Some innovative applications of using blockchain technology are being proposed, and the benefits and challenges of using blockchain technology for education are also discussed.

Threat Matrix: a Fast Algorithm for Human-Machine Chinese Ludo Gaming

Fuji Hana, Man Zhoua, aSchool of Cyber Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

ABSTRACT

Chinese Ludo, also known as Aeroplan Chess, has been a very popular board game for several decades. However, there is no mature algorithm existing for human-machine gambling. The major challenge is the high randomness of the dice rolling, where the algorithm must ensure that the machine is smarter than human, so as to guarantee that the owner of the game machines makes profit. This paper presents a fast Chinese Ludo algorithm (named as “Threat Matrix”) that we have developed recently. Different from most chess programs that rely on high performance computing machines, the evaluation function in our program is only a linear sum of four factors. For fast and low-cost computation, we innovatively construct a concept of the threat matrix by which we can easily obtain the threat between any two dices on any two positions. The threat matrix approach greatly reduces the amount of calculation and enables the program to run on 80x86 SCM of 32bit and 100M, and also supports recursive algorithms to search plies. Statics of matches against human game players shows that, our threat matrix has won on average 92% when there was no time limit, won 95% when the time limit was 10s, and won 98% when the time limit was 5s. Furthermore, the threat matrix can reduce computation by nearly 90% compared to the real-time computing, and memory consumption also drops and is stable, which increases the evaluation speed by 58% compared to the real-time computing.

KEYWORDS

Chinese Ludo, game software, threat matrix, evaluation function.

An Intelligent Mobile Application to Automate the Conversation of Emails to Task Management using AI and Machine Learning

Yi Li¹, Yu Sun², ¹Seattle Academy, 1201 E Union St, Seattle, WA 98122, ²California State Polytechnic University, Pomona, CA, 91768, Irvine, CA 92620

ABSTRACT

It is no secret that a large portion of our population struggle with task management [1]. According to a 2021 research, twenty five percent of people do not employ a task management system and simply work on “whatever seems the most important at the moment”. Among the population that do use some sort of task management, the most popular form of managing personal tasks is through to do lists (33%) followed by using their email inbox (24%). Thus, I thought to combine these two most common methods by creating a to do list automatically generated from the email. We used the open sourced software natural language processing (NLP) to pick out the important sentences in the text and convert them into tasks for the to-do list. We used keywords such as “tomorrow”, “next”, “month”, etc combined with the date the email was sent to determine the “due date” of the to do list. Because the to-do list extracts information directly from the inbox without any participation from a human, unlike many other apps, this could be ef ectively used by those that do not check their email.

KEYWORDS

Mobile platform, Machine Learning, NLP.

The Problem of Error Frequency Distribution in the Miller-Rabin Test for Tripleprime Numbers

Alisher Zhumaniezov, Kazan Federal University, Kazan, Russian Federation

ABSTRACT

This article investigates the error distribution of the Miller-Rabin test for the class of tripleprime numbers. At first the current results on the class of semiprimes are presented. Further, a theoretical estimation of the average frequency for triple prime numbers on an interval is derived, and a comparative analysis with a practical result is demonstrated. Graphs and intermediate conclusions accompany all comparisons. A conclusion is also made about a possible direction for improving this estimation.

KEYWORDS

Miller-Rabin test, strong pseudoprime, number theory, frequency distribution.

TRAC: An Approach to Teaching Security- Aware Programming in Undergraduate Computer Science Courses

Rochelle Elva, Department of Mathematics and Computer Science, Winter Park, Florida, USA

ABSTRACT

The unfortunate list of software failures, attacks, and other software disasters has made it apparent that software engineers, need to produce reliable code. The Department of Homeland Security, reports that 90% of software exploits are due to vulnerabilities resulting from defects in code [1]. These defects are easy to exploit. They are potentially dangerous as they create software vulnerabilities that allow hackers to attack software, preventing it from working or compromising sensitive data [2]. Thus, these defects need to be addressed as part of any effort to secure software. An effective strategy for addressing security-related code defects is to use defensive programming methods like security-aware programming [3]. This paper presents TRAC, an approach to teaching security-aware programming. The acronym stands for Teach, Revisit, Apply and Challenge. It also describes the implementation of the approach and the results of a small case study (n = 21), in a senior-level elective course.

KEYWORDS

Security-Aware Programming, Secure Coding, Software Security, Teaching Secure Coding.

Devising A Cost-Efficient Optical Interconnect for the Remote Metering in Microwave Band

Mikhail E. Belkin, Leonid Zhukov, Nikita Smirnov, and Alexander S. Sigov, MIREA-Russian Technological University, Moscow, Russian Federation

ABSTRACT

In this paper, to provide a long-distant remote measurement of S-parameters for a microwave electronic device under test using a Vector Network Analyzer (VNA), a newer concept to design a cost-efficient bidirectional optical interconnect line is proposed and discussed. The core idea of the concept lies in exploiting two electroabsorption-modulated lasers of the same model for transmitting and receiving in the both directions leveraging the reversible features of an electroabsorption modulator that is able to operate in the modes of electrical-to-optical or optical-to-electrical converter. Another way to improve cost-efficiency of the proposed interconnect line is to implement a single optical fiber to transmit lightwave via downlink and uplink channels using wavelength division multiplexing, which additionally provides complete isolation between the channels. The feasibility and efficiency of the proposed approach are confirmed by proof-of-concept experiments when S-parameters of the widespread microwave amplifiers are measured by comparison in back-to-back or distant mode using a standard electrical VNA.

KEYWORDS

Remote testing, Optical interconnect, S-parameters, Electrical vector network analyzer.

Towards an Appropriate Representation of documents in Information Retrieval: XML Document as a Case

Imane BELAHYANE, Mouad Mammass, Hasna Abioui, Assmaa Moutaoukkil and Ali Idarrou, IRF-SIC Laboratory, Ibn Zohr University, Agadir, 80000, Morocco

ABSTRACT

Nowadays, processing a large mass of documents has become indispensable. Moreover, the abundance of information available today complicates the task of the user to access and find relevant information in a large collection of documents. Thus, the exploitation of large documentary collections requires the implementation of automatic tools allowing a relevant and efficient search. In a practical sense, the relevance of an Information Retrieval System depends on the document representation model. In this paper, we present a comparative study between the most widely known document representation models. We also review Information Retrieval (IR) approaches, within the context of semi-structured documents and more particularly XML documents. The main objective of this study is to highlight the importance of document modeling in the IR process and to show the essential role of graph theory in increasing the meaning of the document representative.

KEYWORDS

Information Retrieval System, Document Structure, Semi-Structured Document, Document Representation Models, XML Document, graph-based approach.

Test Automation for Quality Assurance: A Random Approach

Paul Court and Omar Al-Azzam, Computer Science and Information Technology Department (CSIT), Saint Cloud State University (SCSU), Saint Cloud, MN, USA

ABSTRACT

Testing is a necessary, but sometimes tedious chore for finding faults in software. Finding faults is essential for ensuring quality and reliability. These are valuable traits for a company’s reputation and therefore their financial well-being. An ongoing trade-off between efficiency of testing and the resources necessary for its correct execution will be evaluated in this research. More time will be devoted to an analysis of random test case selection and whether the amount of extra test cases run due to random selection is viable versus the potential time spent fully evaluating the logic for coverage of a generic predicate. The reader will gain knowledge about the expectations for the increase in test cases if randomized selection is employed at some point in the process of testing.

KEYWORDS

Predicate testing, Fault detection, Simulation, Random selection, Logic coverage.

Exploration of the Anti-Corruption System from the Approach of Internet of Things: Evidence from China

Peng Wang^{1, 2} Mei Huang², ¹Department of Public Administration, Sichuan University, Chengdu, China, ²Department of Law, Sichuan Judicial and Police College, Deyang, China

ABSTRACT

Corruption is a stumbling block in the economy and society, and uncovering the concealment of corruption is a crucial aspect of anti-corruption and the prevention of change. We propose a technical and management model of IoT anti-corruption based on the technology enactment framework, which provides a new perspective for building a modern anti-corruption system. Through the case of China, we believe that the IoT anti-corruption system can accurately identify hidden corruption, improve the efficiency of anti-corruption, streamline the organizational structure, and improve the supervision and monitoring system, which can better combat corruption and prevent its occurrence.

KEYWORDS

Internet of Things, Corruption, Technology enactment, Institutional innovation, Monitoring system.

The Effect of Employees ’ Marital Satisfaction on Job Performance: Based on the Perspective of Conservation of Resource Theory

Lijun Sun, Zhefei Mao and Jie Zhou, Institute of Psychology, Chinese Academy of Sciences P. R. China, 100101 and Department of Psychology, University of Chinese Academy of Sciences P. R. China

ABSTRACT

The study linking the marriage with work explores the mechanism of action of employees’ marital satisfaction and job performance through establishing a moderated mediating effect model. The results of the correlation and regression analyses conducted by collecting questionnaires from 290 employees indicated that: (1) Emotional exhaustion and work engagement play a chain mediating role in the positive relationship between marital satisfaction and job performance. (2) Work meaningfulness and work engagement play a chain mediating role in the positive relationship between marital satisfaction and job performance. (3) The need to support a family moderates the relationship between marital satisfaction and work meaningfulness, as well as the mediating effect of work meaningfulness and work engagement on the relationship between marital satisfaction and job performance. (4) The need to support a family moderates the relationship between marital satisfaction and emotional exhaustion, as well as the mediating effect between emotional exhaustion and work engagement on marital satisfaction and job performance. (5) Self-efficacy moderates the relationship between marital satisfaction and work meaningfulness, as well as the mediating effect between work meaningfulness and work engagement on marital satisfaction and job performance. This study provides a new perspective of family as resources for improving employees’ job performance in management.

KEYWORDS

marital satisfaction, job performance, emotional exhaustion, work engagement, work meaningfulness .

Different Companies Stock Price Prediction using LSTM-based Recurrent Neural Network

Sachin Tiwari* and Anoop Kumar Chaturvedi, Department of Computer Science & Engineering, LNCT University, Bhopal, India

ABSTRACT

The issuing, purchase, and sale of shares occur in a stock market, a collection of buyers and sellers. Because of this, it isnt easy to make accurate predictions about the value of a companys shares. The impact of such things may be seen in historical stock prices and historical price data. Because stock data is a time series, a forecasting model may be used to make accurate predictions. An LSTM (long short-term memory) model, a kind of RNN (Recurrent Neural Network), is utilized to predict stock prices. Because of its unique structure, LSTM has no long-term dependencies. According to the research, predictions are based on the low and high prices of different companies. The LSTM-based approach uses a variety of characteristics to make predictions about values and the accuracy of those predictions when made with the help of the LSTM model.

KEYWORDS

LSTM, Forecasting, Stock price prediction, Recurrent Neural Network.

An Infrastructure for Packet-based P2P Networks to Provide Strong Fairness

Mozhgan Asadzadeh¹, Saeid Hosseinzadeh¹ and Mahsa Naghizadeh², ¹Islamic Azad University, Rasht Branch, Rasht, Guilan, Iran, ²Sadjad University, Razavi Khorasan Province, Mashhad, Iran

ABSTRACT

Peer-to-peer networks are powerful forms of communication networks that use their users resources. In this way, with the addition of users, the network also acquires new resources that lead to sustainable network scalability. As a result, the interaction between users and how they access the resources of one another directly affects the network resources. In this paper, we present a new method that divides users of peer-to-peer networks into two main categories, Seeders, and Leechers. Depending on the type of function of each user, their roles belong to one of these categories. Based on these roles, Seeders, which are benevolent users of the network, will have more control over network transactions and share their resources only when needed. On the other hand, Leechers, who are regular network users, utilize the T4T method to send and receive packets. In order for new entrants to be able to enter the transactions, they use a new method that puts them in the role of a middleman. Our experiments show that due to the use of resources that were not previously available to the network, this method can provide higher performance for many aspects of the network.

KEYWORDS

P2P, fairness, T4T, BitTorrent, Leecher, Seeder, file-sharing.

A Survey: Recommendation Systems and AI

Samar Hendawi, Dr.Abdelfatah A.Tamimi and Dr.Shadi Alzi’bi, Faculty of Science and Information Technology – Al Zaytoonah University of Jordan

ABSTRACT

Recommendation systems are an essential feature in our digital world, where users are often overwhelmed with choices and need help finding what they are looking for. Recommendation systems are now so popular that many of us use them without even knowing it. Since we cannot search all the products or content on a website, the recommendation system plays an important role in helping us have a better user experience. Artificial intelligence (AI) [4], particularly computational intelligence and machine learning methods and algorithms have been naturally applied in the development ofrecommendation systems to improve prediction accuracy. This paper offers a comparative study of approaches in recommendation systems, starting with a general presentation of each one, and then it treats the advantages, the limitations, and the techniques. The observations in this paper will directly support researchers and professionals to better understand current developments and new directions in the field of recommendation systems using AI.

KEYWORDS

Recommendation system, Content-based filtering, Collaborative filtering, Hybrid filtering, Cold start, Sparsity, Scalability.

Text-to-Face Generation with Stylegan2

D. M. A. Ayanthi, Sarasi Munasinghe, Department of Computer Science, Faculty of Science, University of Ruhuna, Wellamadama, Matara, Sri Lanka

ABSTRACT

Synthesizing images from text descriptions has become an active research area with the advent of Generative Adversarial Networks. The main goal here is to generate photo-realistic images that are aligned with the input descriptions. Text-to-Face generation(T2F) is a sub-domain of Text-to-Image generation(T2I) that is more challenging due to the complexity and variation of facial attributes. It has a number of applications mainly in the domain of public safety. Even though several models are available for T2F, there is still the need to improve the image quality and the semantic alignment. In this research, we propose a novel framework, to generate facial images that are well-aligned with the input descriptions. Our framework utilizes the high-resolution face generator, StyleGAN2, and explores the possibility of using it in T2F. Here, we embed text in the input latent space of StyleGAN2 using BERT embeddings and oversee the generation of facial images using text descriptions. We trained our framework on attribute-based descriptions to generate images of 1024x1024 in resolution. The images generated exhibit a 57% similarity to the ground truth images, with a face semantic distance of 0.92, outperforming state-of-the-artwork. The generated images have a FID score of 118.097 and the experimental results show that our model generates promising images.

KEYWORDS

Text-to-Face Generation, StyleGAN2, High-Resolution, Semantic Alignment, Perceptual Loss.

RF Signal Amplifier on MOS Synthetic Inductance

Vahe Buniatyan¹, Norayr Martirosyan^1,2, Mkrtich Yeranosyan³ and Grigor Travadjyan¹, ¹National Polytechnic University of Armenia, 105 Teryan Street, 375009, Yerevan, Armenia, ²Center for the Advancement of Natural Discoveries using Light Emission (CANDLE Synchrotron Research Institute), 31 Acharyan Str., 0022 Yerevan, Armenia, ³Institute of Chemical Physics (after A. Nalbandyan) NAS Armenia, Yerevan

ABSTRACT

An active circuit was offered which exhibits synthetic inductive effect. Distinctive feature from the well known realizations is to be based on the universality of the MOS transistor as a typical circuit element. The simulated inductor is compatible with a modern MOS technology. The inductive effect can be controlled and adjusted by changing the R and C parameters of MOS structures in RC feedback loop. It is shown that at a certain frequency region the active resistance of circuit demonstrates negative values depending on R and C of MOS transistors (in turn the geometric sizes). This is an important circumstance, since the gate width is an efficient design parameter of the CMOS IC. The amplitude-frequency, phase-frequency characteristics and amplifier properties of the circuit were simulated. The circuit results in a voltage-co ferroelectric, emission controlled amplifier with adjustable parameters.

KEYWORDS

synthetic inductive effect, MOS transistor, voltage-controlled amplifier.