Engineering Commons^™

Modulation Recognition Method Of Mixed Signal Based On Intelligent Analysis Of Cyclic Spectrum Section, Yu Du, Xinquan Yang, Jianhua Zhang, Suchun Yuan, Huachao Xiao, Jingjing Yuan

Journal of System Simulation

Abstract: Aiming at the problems of low intelligence and poor adaptability for the existing mixed signal recognition methods, an intelligent recognition method based on cyclic spectral cross section and deep learning is proposed. For common mixed communication signals, the characteristics of zero frequency cross section of cyclic spectrum are theoretically deduced and analyzed. Two new pre-processing methods, nonlinear segmental mapping and directional pseudo-clustering are proposed, which can effectively improve the adaptability and consistency of cross section features. The pre-processed feature graph is combined with the residual network (ResNet), and the deep learning network is used to mine and analyze the …

Short-Time Human Activity Recognition Based On Wavelet Features Matching, Benyue Su, Li Zhang, Qingxuan He, Min Sheng

Journal of System Simulation

Abstract: The selection of features is the key problem in the study of human activity recognition. In order to obtain sufficient and stable behavioral features, long-time behavioral data that exceed one behavior cycle are often processed, while short-time behavioral data with less than one behavioral cycle are usually unstable, making it difficult to achieve accurate and stable identification. This paper proposes a short-time human activity recognition method based on the combination of wavelet transform and template matching. Coefficient features are extracted using wavelet transform method. The features of the short-time test samples are matched with the features in the template …

Scheduling Optimization Of Aluminum Extrusion Production Line Based On Timed Petri Net And Bso Algorithm, Yali Wu, Shuting He, Yanxi Yang, Lianqiang Feng, Fuqiang Wang, Yulu Chen

Journal of System Simulation

Abstract: For the problems of long production period and low efficiency caused by the complicated processes and large scheduling capacity of aluminum extrusion production line in industrial production, a timed Petri net (TdPN) scheduling model of aluminum extrusion production line is proposed and analyzed for reasonableness. The brain storm optimization (BSO) algorithm is introduced into the model, and an optimized scheduling algorithm for aluminum extrusion scheduling problems is proposed based on the individual encoding and decoding methods. The simulated annealing local search mechanism is used to improve the performance of BSO algorithm in the later stage, which can achieve the …

A Multi-Resolution Simulation Modeling Method, Zhaopeng Liu, Xinhai Xu, Bowen Yuan, Jinlu Zhang

Journal of System Simulation

Abstract: Aiming at the resolution gap between the operation task issued by the high-level commanders and the simulation system model instructions in the human-in-the-loop simulation deduction, a multi-resolution modeling method based on behavior tree is proposed. By improving the behavior tree syntax, the low-resolution combat missions are disaggregated into high-resolution simulation system instructions. By designing a decision model embedded in the behavior tree, the problem of resource uncertainty and execution effect uncertainty faced in the execution of model instructions is solved. A combat scenario for seizing air supremacy is designed to verify the effectiveness of the method.

Data Integration Based Human Activity Recognition Using Deep Learning Models, Basamma Umesh Patil, D V Ashoka, Ajay Prakash B. V

Karbala International Journal of Modern Science

Regular monitoring of physical activities such as walking, jogging, sitting, and standing will help reduce the risk of many diseases like cardiovascular complications, obesity, and diabetes. Recently, much research showed that the effective development of Human Activity Recognition (HAR) will help in monitoring the physical activities of people and aid in human healthcare. In this concern, deep learning models with a novel automated hyperparameter generator are proposed and implemented to predict human activities such as walking, jogging, walking upstairs, walking downstairs, sitting, and standing more precisely and robustly. Conventional HAR systems are unable to manage real-time changes in the surrounding …

The Security And Cyber Defence Realities And Difficulties In Algeria, Kada Aicha

Journal of Police and Legal Sciences

This research paper aims to shed light on the digital challenge faced by Algeria as it enters the world of the knowledge society, which qualifies it to achieve cybersecurity and cyber defense against various forms and types of security threats, including cyber threats. The researcher used an analytical approach to understand the phenomenon under study and trace its causes, in addition to a case study method to study all aspects of the studied phenomenon and identify the characteristics of the case study - Algeria was chosen as the analysis unit. The study concluded several important results, including:

The deficiency of …

Social Networks And Their Impact On Social Values In The United Arab Emirates, Muhammad Al-Naqbi

Journal of Police and Legal Sciences

The aim of the study was to measure the impact of social media networks on societal values in the United Arab Emirates (UAE). To achieve this objective, a questionnaire was used as a data collection tool, and a social survey method was employed within the study's community. A simple random sample of 50 individuals was selected from the employees of the Social Support Center and the Department of Social Care in the Emirate of Sharjah. The study found several results, including that social media networks contribute to learning unethical behaviors and the spread of extremist ideas that affect societal values. …

Enhancing Student'sperformance Classification Using Ensemble Modeling, Ahmed Adil Nafea, Muthanna Mishlish, Ali Muwafaq Haban Shaban, Mohammed M. Al-Ani, Khattab M Ali Alheeti, Hussam J. Mohammed

Iraqi Journal for Computer Science and Mathematics

A precise prediction of student performance is an important aspect withineducational institutions to improve results and provide personalized support ofstudents.However, the predication accuracy of student performance considers anopenissue within education field.Therefore, thispaper proposes a developedapproachto identifyperformance of students using a group modeling. This approach combinesthe strengths of multiple algorithms including random forest (RF), decision tree (DT), AdaBoosts, and support vector machine (SVM). Afterward, thelastensemble estimatesas one of the bets logistic regressionmethodswas utilizedto create a robust and reliable predictive modelbecause it considers The experiments were evaluated usingtheOpen University Learning Analytics Dataset (OULAD)benchmark dataset.The OULADdataset considersa comprehensive dataset containingvarious characteristics related to …

A Comprehensive Review Of Software Development Life Cycle Methodologies: Pros, Cons, And Future Directions, Qahtan M. Yas, Abdulbasit Alazzawi, Bahbibi Rahmatullah

Iraqi Journal for Computer Science and Mathematics

The software development process needs specific and studied steps within a reliable plan to achieve the requirements for the success of any project. Software development life cycle (SDLC) methodologies have provided several models that meet the needs of the various proposed projects. These methodologies present various scenarios that can be applied in the process of developing systems to make them more efficient and predictive. The paper aims to illuminate the paramount Software Development Life Cycle (SDLC) methodologies by conducting a comprehensive review of the pros andcons of the various models widely used for software design. Furthermore, the paper discusses fundamental …

An Investigation Of Internet Service Provider Selection Criteria: A Systematic Literature Review, Mohammed Naji, Sivadass Thiruchelvam, Mohammed Khudari

Iraqi Journal for Computer Science and Mathematics

In today’s highly competitive business environment, organizations need to collaborate with strategic partners to provide high quality services at lower costtomaintain theireffectiveness and efficiency. Hence,the concept of Supply Chain Management (SCM)emergedto effectively coordinate and manage financial, physical,and information activities within organizations andamong all participating external parties. Qualitative and quantitative factors must be consideredin SCM toensuresustainable development. The research investigatesthe metricsand criteria influencing the process of selecting an Internet service provider (ISP) from 2001 to2022to assessthe extent to which these factors have met the requirements of current decision-makers. It is appropriate to conduct a literature review in light of the changes …

State Of The Art In Drivers’ Attention Monitoring – A Systematic Literature Review, Sama Hussein Al-Gburi, Kanar Alaa Al-Sammak, Ion Marghescu, Claudia Cristina Oprea

Karbala International Journal of Modern Science

Recently, driver inattention has become the leading cause of automobile accidents. As a result, the driver's perception and decision-making abilities are diminished, and the driver can lose control of the car. To prevent accidents caused by driver inattention, it’s vital to continuously monitor the driver and his driving behaviour and inform him if he becomes distracted or sleepy. This topic has been the subject of study for decades. Whenever feasible to recognise unsafe driving in advance, accidents could be avoided. This document presents an overview of the existing driver alertness system and the various techniques for detecting driver attentiveness.

An Intelligent Prairie Dog Optimization (Ipdo) And Deep Auto-Neural Network (Dann) Based Ids For Wsn Security, D Hemanand, P Mohankumar, N Manoj Kumar, S Vaitheki, P Saranya

Iraqi Journal for Computer Science and Mathematics

Wireless sensor networks (WSNs) are targets of intrusion, which seeks to make these networks lesscapable of performing their duties or even completely eradicate them. The Intrusion Detection System (IDS) is highly important for WSN, sinceit aids in the identification and detection of harmful attacks that impair the network's regular functionality. In order to strengthen the security of WSN, several machine learning and deep learning approaches are employed in thetraditional works. However, its main drawbacks are computational burden, system complexity, poornetwork performance outcomes, and high falsealarms. Therefore, the goal of this study is to develop an intelligent IDS framework for significantly …

Perspectives On Design Considerations Inspired By Security And Quantum Technology In Cyberphysical Systems For Process Engineering, Helen Durand, Jihan Abou Halloun, Kip Nieman, Keshav Kasturi Rangan

Chemical Engineering and Materials Science Faculty Research Publications

Advances in computer science have been a driving force for change in process systems engineering for decades. Faster computers, expanded computing resources, simulation software, and improved optimization algorithms have all changed chemical engineers’ abilities to predict, control, and optimize process systems. Two newer areas relevant to computer science that are impacting process systems engineering are cybersecurity and quantum computing. This work reviews some of our group’s recent work in control-theoretic approaches to control system cybersecurity and touches upon the use of quantum computers, with perspectives on the relationships between process design and control when cybersecurity and quantum technologies are of …

"Semiclassical Mastermind", Curtis Bair, Alexa S. Cunningham, Joshua Qualls

Posters-at-the-Capitol

Games are often used in the classroom to teach mathematical and physical concepts. Yet the available activities used to introduce quantum mechanics are often overwhelming even to upper-level students. Further, the "games" in question range in focus and complexity from superficial introductions to games where quantum strategies result in decidedly nonclassical advantages, making it nearly impossible for people interested in quantum mechanics to have a simple introduction to the topic. In this talk, we introduce a straightforward and newly developed "Semiclassical Mastermind" based on the original version of mastermind but replace the colored pegs with 6 possible qubits (x+, x-, …

Disaster Area Lora Mesh Communications, Joel Bulkley

Posters-at-the-Capitol

In the aftermath of the December 2021 tornado disaster that swept through Western Kentucky causing 57 fatalities, 500+ critical injuries, and millions of dollars’ worth in damages, one of the first things that became apparent was the lack of communications and underlying power infrastructure. The goal of this project is to create a highly resilient LoRa mesh communications network utilizing low power, low cost, and easy to deploy radios to enable first responders in a disaster area to communicate their location and send critical information in a situation where traditional communications infrastructure has been destroyed or is otherwise unavailable. Through …

Analyzing The User'ssentiments Of Chatgpt Using Twitter Data, Tarık Talan, Adem Korkmaz, Cemal Aktürk

Iraqi Journal for Computer Science and Mathematics

ChatGPT, an advanced language model based on artificial intelligence developed by the OpenAI, was released to internet users on November 30, 2022, and has attracted a great deal of attention. The feelings and thoughts of those who first experienced ChatGPT are valuable feedback for evaluating the success and positive and negative aspects of this technology. In this study, sentiment analysis of ChatGPT-themed tweets onTwitter was conducted to comprehensively evaluate the feelings and thoughts of users during the first two months following the announcement of ChatGPT. Approximately 788.000 English tweets were analyzed using the AFINN, Bing, and NRC sentiment dictionaries. The …

Gesture-Based American Sign Language (Asl) Translation System, Kayleigh Moore, Stefano Pecile, Mahdi Yazdanpour

Posters-at-the-Capitol

According to the World Health Organization (WHO), over 5% of the world's population experiences severe hearing loss. Approximately 9 million people in the U.S. are either functionally deaf or have mild-to-severe hearing loss. In this research, we designed and implemented a translation interface which turns American Sign Language (ASL) gestures captured from a pair of soft robotic gloves into text and speech instantaneously.

We used a combination of flex sensors, tactile sensors, and accelerometers to recognize hand gestures and to record hand and fingers positions, movements, and orientations. The digitized captured gestures were then sent to our proposed translation interface …

A Literature Review On Agile Methodologies Quality, Extreme Programming And Scrum, Naglaa A. Eldanasory, Engy Yehia, Amira M. Idrees

Future Computing and Informatics Journal

most applied methods in the software development industry. However, agile methodologies face some challenges such as less documentation and wasting time considering changes. This review presents how the previous studies attempted to cover issues of agile methodologies and the modifications in the performance of agile methodologies. The paper also highlights unresolved issues to get the attention of developers, researchers, and software practitioners.

Enhancing Query Processing On Stock Market Cloud-Based Database, Hagger Essam, Ahmed G. Elish, Essam M. Shaban

Future Computing and Informatics Journal

Cloud computing is rapidly expanding because it allows users to save the development and implementation time on their work. It also reduces the maintenance and operational costs of the used systems. Furthermore, it enables the elastic use of any resource rather than estimating workload, which may be inaccurate, as database systems can benefit from such a trend. In this paper, we propose an algorithm that allocates the materialized view over cloud-based replica sets to enhance the database system's performance in stock market using a Peer-to-Peer architecture. The results show that the proposed model improves the query processing time and network …

Current Topics In Technology-Enabled Stroke Rehabilitation And Reintegration: A Scoping Review And Content Analysis, Katryna Cisek

Articles

Background. There is a worldwide health crisis stemming from the rising incidence of various debilitating chronic diseases, with stroke as a leading contributor. Chronic stroke management encompasses rehabilitation and reintegration, and can require decades of personalized medicine and care. Information technology (IT) tools have the potential to support individuals managing chronic stroke symptoms. Objectives. This scoping review identifies prevalent topics and concepts in research literature on IT technology for stroke rehabilitation and reintegration, utilizing content analysis, based on topic modelling techniques from natural language processing to identify gaps in this literature. Eligibility Criteria. Our methodological search initially identified over 14,000 …

Know An Emotion By The Company It Keeps: Word Embeddings From Reddit/Coronavirus, Alejandro García-Rudolph, David Sanchez-Pinsach, Dietmar Frey, Eloy Opisso, Katryna Cisek, John Kelleher

Articles

Social media is a crucial communication tool (e.g., with 430 million monthly active users in online forums such as Reddit), being an objective of Natural Language Processing (NLP) techniques. One of them (word embeddings) is based on the quotation, “You shall know a word by the company it keeps,” highlighting the importance of context in NLP. Meanwhile, “Context is everything in Emotion Research.” Therefore, we aimed to train a model (W2V) for generating word associations (also known as embeddings) using a popular Coronavirus Reddit forum, validate them using public evidence and apply them to the discovery of context for specific …

A Path Planning Framework For Multi-Agent Robotic Systems Based On Multivariate Skew-Normal Distributions, Peter Estephan

Theses, Dissertations and Capstones

This thesis presents a path planning framework for a very-large-scale robotic (VLSR) system in an known obstacle environment, where the time-varying distributions of agents are applied to represent the multi-agent robotic system (MARS). A novel family of the multivariate skew-normal (MVSN) distributions is proposed based on the Bernoulli random field (BRF) referred to as the Bernoulli-random-field based skew-normal (BRF-SN) distribution. The proposed distributions are applied to model the agents’ distributions in an obstacle-deployed environment, where the obstacle effect is represented by a skew function and separated from the no-obstacle agents’ distributions. First, the obstacle layout is represented by a Hilbert …

Encryption And Compression Classification Of Internet Of Things Traffic, Mariam Najdat M Saleh

Browse all Theses and Dissertations

The Internet of Things (IoT) is used in many fields that generate sensitive data, such as healthcare and surveillance. Increased reliance on IoT raised serious information security concerns. This dissertation presents three systems for analyzing and classifying IoT traffic using Deep Learning (DL) models, and a large dataset is built for systems training and evaluation. The first system studies the effect of combining raw data and engineered features to optimize the classification of encrypted and compressed IoT traffic using Engineered Features Classification (EFC), Raw Data Classification (RDC), and combined Raw Data and Engineered Features Classification (RDEFC) approaches. Our results demonstrate …

Efficient Cloud-Based Ml-Approach For Safe Smart Cities, Niveshitha Niveshitha

Browse all Theses and Dissertations

Smart cities have emerged to tackle many critical problems that can thwart the overwhelming urbanization process, such as traffic jams, environmental pollution, expensive health care, and increasing energy demand. This Master thesis proposes efficient and high-quality cloud-based machine-learning solutions for efficient and sustainable smart cities environment. Different supervised machine-learning models for air quality predication (AQP) in efficient and sustainable smart cities environment is developed. For that, ML-based techniques are implemented using cloud-based solutions. For example, regression and classification methods are implemented using distributed cloud computing to forecast air execution time and accuracy of the implemented ML solution. These models are …

Contributors To Pathologic Depolarization In Myotonia Congenita, Jessica Hope Myers

Browse all Theses and Dissertations

Myotonia congenita is an inherited skeletal muscle disorder caused by loss-of-function mutation in the CLCN1 gene. This gene encodes the ClC-1 chloride channel, which is almost exclusively expressed in skeletal muscle where it acts to stabilize the resting membrane potential. Loss of this chloride channel leads to skeletal muscle hyperexcitability, resulting in involuntary muscle action potentials (myotonic discharges) seen clinically as muscle stiffness (myotonia). Stiffness affects the limb and facial muscles, though specific muscle involvement can vary between patients. Interestingly, respiratory distress is not part of this disease despite muscles of respiration such as the diaphragm muscle also carrying this …

The Effects Of Disinformation Upon National Attitudes Towards The Eu And Its Institutions, Alex Murphy

Dissertations

This work explores the effects of misinformation and disinformation upon national attitudes towards the EU. Several nations, in particular the Russian Federation, have been working for decades to spread narratives that debase the political processes of healthy democracies around the world. There is strong evidence to show that extensive efforts have been made to disrupt the inner workings and overall membership of the EU, to support disruptive policies in the United States such that political deadlock is maintained indefinitely. These efforts are largely based on the spreading of misinformation and disinformation across social networks that have done very little to …

Development Of A Hospital Discharge Planning System Augmented With A Neural Clinical Decision Support Engine, David Mulqueen

Dissertations

The process of discharging patients from a tertiary care hospital, is one of the key activities to ensure the efficient and effective operation of a hospital. However, the decision to discharge a patient from a hospital is complex, as it requires multiple interactions with nurses, family, consultants, health information records and doctors, which can be very time consuming and prone to error. This thesis descries how a neural network based Clinical Decision Support system can be developed, to help in the decision making process and dramatically reduce the time and effort in running the discharge process in a hospital. A …

Forecasting Covid-19 Cases Using Dynamic Time Warping And Incremental Machine Learning Methods, Luis Miralles-Pechuán, Ankit Kumar, Andres L. Suarez-Cetrulo

Articles

The investment of time and resources for developing better strategies is key to dealing with future pandemics. In this work, we recreated the situation of COVID-19 across the year 2020, when the pandemic started spreading worldwide. We conducted experiments to predict the coronavirus cases for the 50 countries with the most cases during 2020. We compared the performance of state-of-the-art machine learning algorithms, such as long-short-term memory networks, against that of online incremental machine learning algorithms. To find the best strategy, we performed experiments to test three different approaches. In the first approach (single-country), we trained each model using data …

Artificial Emotional Intelligence In Socially Assistive Robots, Hojjat Abdollahi

Electronic Theses and Dissertations

Artificial Emotional Intelligence (AEI) bridges the gap between humans and machines by demonstrating empathy and affection towards each other. This is achieved by evaluating the emotional state of human users, adapting the machine’s behavior to them, and hence giving an appropriate response to those emotions. AEI is part of a larger field of studies called Affective Computing. Affective computing is the integration of artificial intelligence, psychology, robotics, biometrics, and many more fields of study. The main component in AEI and affective computing is emotion, and how we can utilize emotion to create a more natural and productive relationship between humans …

Understanding And Quantifying Human Factors In Programming From Demonstration: A User Study Proposal, Shakra Mehak, Aayush Jain, John D. Kelleher, Philip Long, Michael Guilfoyle, Maria Chiara Leva

Conference papers

Programming by demonstration (PbD) is a promising method for robots to learn from direct, non-expert human interaction. This approach enables the interactive transfer of human skills to the robot. As the non-expert user is at the center of PbD, the efficacy of the learned skill is largely dependent on the demonstrations provided. Although PbD methods have been extensively developed and validated in the field of robotics, there has been inadequate confirmation of their effectiveness from the perspective of human teachability. To address this gap, we propose to experimentally investigate the impact of communicating robot learning process on the efficacy of …