Computer Engineering Commons^™

An Intelligent System Using Deep Learning For Healthcare Monitoring In Light Of The Covid-19 And Future Pandemics Based On Iot, Sara Salman Qasim, Rajaa J. Khanjar, Jamal Nasir Hasoon, Baesher Abdullateff Abad, Ali Hussein Fadil, Shajan.M. Alsowaidi

Al-Esraa University College Journal for Engineering Sciences

Recently, the Internet of Things has become a compelling research field as a new topic of research in various disciplines, particularly in the field of healthcare, because the Internet of Things is rebuilding modern healthcare systems by integrating technology, economics, and social perspectives. The development of healthcare systems from traditional to more personalized systems in which patients can be easily diagnosed, monitored and treated and many people can be helped. People are treated and cared for remotely and this is what some people need in the crisis the world has been through like COVID-19. This epidemic is caused by the …

Speech Coding Based On A Hybrid Approach: Dct, Huffman And Run-Length Coding, Sundos Abdulameer Alazawi, Esraa Jaffar Baker, Shahbaa Mohammed Abdulmaged

Al-Esraa University College Journal for Engineering Sciences

The exponential expansion of data in the digital world necessitates the development of effective methods for data transmission and storage. Data compression (DC) strategies are suggested to reduce the quantity of data stored or conveyed due to constrained resources. As a result of DC ideas' ability to efficiently use existing storage space and transmission capacity, different methods have been developed in various areas. Speech coding is a lossy method of coding; therefore, the output signal differs slightly from the input signal. Speech coding is useful for message encryption, communication over long distances and speech quality. In the fields of digital …

The Evolution Of The University Of Al-Anbar Urban Planning Based On The Mental Picture's Diversity And The Contemporary Planning Spaces Filling, Ali Abdulsamea Hameed

Al-Esraa University College Journal for Engineering Sciences

This research concentrated on the role of environmental graphic design (EGD) and the place-making idea in the interior environment of Al-Anbar University. To develop an identity being visual for the University of Al-Anbar internal environment (IE) is this goal of project. The search sought to create and enhance the shape of the internal University of Al-Anbar vacuum by reviving the idea of place building. In order to build and filling the internal emptiness of the campus, the identity being visual must also be strengthened. The descriptive analytical approach was chosen since it was best suited to achieve the goal of …

Optimal Operation Scheduling Of Integrated Energy System Considering Energy Priority, Dongli Jia, Keyan Liu, Zhaoying Ren, Zezhou Wang, Dongsheng Tang

Journal of System Simulation

Abstract: Integrated with the actual situation of power grid and the growth of new energy, a multiobjective model for optimal scheduling of the integrated energy system(IES) is established based on the analysis of the energy-flow relationship of the IES and taking into account the priority of energy utilization and the load demand response in terms of the mismatch between the distributed energy sources and the loads, the net benefit of the unit cost of the IES, and the load response degree. Combined with the equipment and the environmental benefits system, a priority constraint for energy utilization has been established for …

Research On Scheduling Strategies Simulation For Building Air-Conditioning Systems Based On Transfer Imitation Learning, Qiaochu Wang, Yan Ding, Chuanzhi Liang, Haozheng Zhang, Chen Huang

Journal of System Simulation

Abstract: To solve the problem of unstable performance and inefficient training process of low-quality data conditions at the initial stage of online deployment of air conditioner scheduling, we propose a migration-imitation learning-based air conditioning scheduling strategy simulation method. Reinforcement learning methods are used to generate building operation strategies. A standard building simulation model serves as the source domain, upon which migration learning is applied. An imitation learning loss function is incorporated into the intelligent loss function to enhance algorithm performance. The results indicate that, compared with the non-use of migration learning, the proposed method can improve the operational efficiency by …

Behavioral Modeling Of Manned-Unmanned Cooperative Air Combat Based On Improved Abc Algorithm, Peng Wang, Haoyu Liu, Ni Li, Zexi Yu, Shangjie Jia

Journal of System Simulation

Abstract: To solve the problem of difficulty in establishing collaborative behavior models and weak adversarial capabilities in typical MAV/UAV air combat scenarios, a mixed decision based MAV/UAV behavior modeling framework is proposed. Using collaborative rule sets, rule subsets, tactical action sets, and other tools, a hierarchical decision collaborative behavior model supporting five types of collaborative tactics, including grinding tactics and unilateral flanking tactics, is constructed in this framework. a behavior model parameter optimization method based on an improved artificial bee colony (ABC) algorithm is proposed. By using the Mason rotation method to initialize the population, a better initial honey source …

Harmonic Impedance Modeling And Oscillation Analysis Of Modular Multilevel Converter, Yuhong Wang, Wensheng Chen, Shilin Gao, Jianquan Liao, Yangfan Cheng

Journal of System Simulation

Abstract: To facilitate rapid analysis of the oscillation stability mechanism in modular multilevel converter-based high voltage direct current (MMC-HVDC) systems and streamline the simulation process for determining MMC impedance characteristics, a simplified mathematical simulation model for MMC closed-loop impedance is developed using the harmonic state space method. This model considers various control strategies and includes both AC-side and DC-side impedance models. By applying a Nyquist criterion-based impedance analysis method, the stability mechanisms on the AC and DC sides of the MMC are examined. In addition, a data-driven oscillation stability analysis method is also proposed, leveraging a global sensitivity algorithm based …

Path Planning Of Desert Robot Based On Deep Reinforcement Learning, Ming Li, Wangzhong Ye, Jiehua Yan

Journal of System Simulation

Abstract: Due to the complexity and variability of the desert environment, the key to the high-efficient of mobile robot is how to avoid obstacles and plan its path. To solve the problems of poor search efficiency and slow convergence of deep reinforcement learning algorithm in complex environment, an improved deep reinforcement learning path planning algorithm is proposed. The exploration factor is improved and dynamically adjusted according to the convergence degree of the algorithm, so that the exploration factor dynamically decreases with the increase of the understanding degree of the agent to the environment, thus speeding up the convergence speed of …

Fail-Safe Logic Design Strategies Within Modern Fpga Architectures, Priya A. Bhakta

Electrical and Computer Engineering ETDs

Field Programmable Gate Arrays (FPGAs) are vulnerable to radiation-induced single event upsets (SEUs) and fault injection attacks, requiring the use of redundancy techniques such as fail-safe computing. Fail-safe computing refers to computing systems that revert to a non-operational safe state when a fault occurs. This work investigates circuit-level techniques and implements fail-safe computing processes as mitigation for SEUs and fault injection attacks on FPGAs. The analysis reveals vulnerabilities that exist in FPGAs over those in application-specific integrated circuits (ASIC); thus, requiring a more elaborate network of redundant circuits and checking logic. The reconfiguration capability of FPGAs adds complexity to fail-safe …

Enhancing Bedside Nursing Care: An Artificial Neural Network Approach To Predicting Cardiac Arrest In Hospitalized Adults, Katharine Czech, Alec Pannunzio, Maddie Anderson, Numair Khan, Jacob Lacanienta, Jonghyeok Lee, Aneesh Poddutur, Emily Rastovski, Kira Voelker, Julie Wasyliw, Sei Zou

The Journal of Purdue Undergraduate Research

No abstract provided.

Secure Healthcare Systems And Big Data: A Bibliometrics Analysis, Rasha Talal Hameed, Saad Ahmed Dheyab, Saba Abdulbaqi Salman, Ahmed Hussein Ali, Omar Abdulwahabe Mohamad

Iraqi Journal for Computer Science and Mathematics

This study conducts a bibliometrics analysis of research on secure healthcare systems and big data, aiming to identify trends, key contributors, and thematic areas within the field. By examining a comprehensive database of academic publications, we highlight the evolution of research from foundational concepts to contemporary innovations in data security and privacy management in healthcare. Key metrics such as publication volume, citation impact, and co-authorship networks are analyzed to uncover the most influential authors and institutions. Additionally, we explore the integration of big data analytics in enhancing healthcare delivery while addressing security challenges. The findings provide valuable insights for researchers …

Integrating Image Data Fusion And Resnet Method For Accurate Fish Freshness Classification, Yahya Layth Khaleel, Mustafa Abdulfattah Habeeb, Ghadeer Ghazi Shayea

Iraqi Journal for Computer Science and Mathematics

Fish freshness classification is critical for protecting public health and ensuring efficient economic, regulatory and environmental sustainability. Classifying accurately reduces the risk of foodborne illness, protects product quality, builds consumer trust and supports sustainable resource conservation through waste minimization. However, the traditional methods for determining fish freshness are variable, time consuming and subjective, precluding practical use. This research presents an improved framework that integrates image data fusion and a deep learning ResNet model to differentiate fresh and nonfresh fish. From multiple sources, a comprehensive dataset including 16,640 samples was curated, and data fusion was used to increase the diversity and …

Energy Efficiency Optimization In Integrated Sensing And Communication Networks, Arianna M. Santamaria Penafiel

Electrical and Computer Engineering ETDs

In the emerging landscape of Integrated Sensing and Communication (ISAC) networks, achieving energy efficiency while concurrently performing sensing and communication tasks remains challenging. This paper introduces a new framework, a novel solution that empowers User Equipment (UEs) to make informed decisions regarding their transmission power allocation, optimizing the energy efficiency of sensing, communication, and data reporting to the gNB (gNodeB) functions. Initially, a novel ISAC network paradigm is proposed, where the gNB employs rewards, such as monetary incentives, to motivate UEs to engage in sensing, data collection, and reporting within its coverage area based on the principles of Contract Theory. …

Utilizing Pupper, A Social Robot Dog, To Increase Happiness And Improve Mood In Pediatric Patients Of A Cardiac Step-Down Unit, Angela Feng Wu

Master's Projects and Capstones

Objective The usage of social robots in pediatrics is an emerging field of study. Preliminary research shows that they are effective at improving the psychosocial well-being of pediatric patients. This quality improvement project focuses on Pupper, a newly developed quadruped social robot dog, and its ability in improving mood and happiness in pediatric patients of a cardiac step-down unit. Aim The aim of this project is to increase average mood scores of pediatric cardiac patients aged 3-25 years by 50% from their baseline of 3.75 to 5.63 on a six-point scale within a one-month time frame. Methods Before intervention and …

Predicting Vegetation Override Force For Off-Road Autonomy, Marc Nicholas Moore

Theses and Dissertations

Vegetation override is an important aspect of off-road ground vehicle mobility. An autonomous ground vehicle’s (AGV) perception system must distinguish between vegetation that can be easily driven through from vegetation that cannot. Predicting the resistance of vegetation could allow path- planning systems to make this distinction. However, despite its importance, direct measurement of vegetation resistance is rare, as most studies use indirect proprioceptive data, such as inertial measurements, as proxies for override force. Notably, there is a lack of empirical data on the override resistance of small stems (< 2.5 cm) and clusters of vegetation on medium-sized (approx. 1000kg) vehicles. To address this gap, a comprehensive dataset of override measurements was collected for clumps of small vegetation relevant to intermediate-sized AGVs navigating off-road terrain. This dataset includes over 70 recordings using the Robot Operating System (ROS) during controlled driving experiments through small trees, grasses, and bushes. The collected data includes light detection and ranging (LiDAR) scans, imagery, force measurements from integrated load cells, and simultaneous localization and mapping (SLAM) information. A key contribution of this research is the development and calibration of a custom push bar system equipped with load cells to directly measure override forces. These measurements are compared to empirical models previously developed by the U.S. Army Corps of Engineers for larger single-stem vegetation. A preprocessing pipeline was developed to automatically extract and label LiDAR and camera data according to these force measurements. This self-labeled dataset was then used to train machine learning models that predict override resistance of vegetation from LiDAR and camera scans alone. This research characterizes the relationship between override forces and the observable features of vegetation as measured by LiDAR and camera sensors. Deep learning models were developed and trained to predict override forces based on different input modalities and features derived from point clouds and images. The performance of these models was compared across various input features to investigate how deep learning can create a generalizable and accurate force prediction system.

The Role Of Customers In Strategic Information Technology (It) Initiatives, Siddharth Aggarwal

Doctoral Dissertations and Projects

This study focused on a small organization in the United States of America. The organization has IT departments that cater to the IT needs of its internal and external customers through IT products and services. Such organizations run full life cycles of product management and product development and often face off with situations to prioritize the use of their limited resources. Ideally, organizations focus on strategic IT initiatives that might be in the company's and its customers' best interest. However, instances occur when IT-driven initiatives lose that focus and might end up diverting resources toward the latest shiny technology and …

An Evaluation Of Features Extracted From Facial Images In The Context Of Accurate Age Estimation⋆, Malik Awais Khan, Aurelia Power, Peter Corcoran, Christina Thorpe

Conference papers

Age estimation by face image recognition can be used in numerous ways with regression models to manage access control, improve security, and guarantee the protection of children online. The approaches used for predicting age—including data selection, cleaning techniques, feature extraction, algorithm choice, and hyperparameter tuning—often struggles with generalization. Furthermore, a lot of methods neglect to specifically address how extracted face features might be used for prediction. To address the lack of racial diversity we acquired a dataset consisting of different races from literature. We also examined the ability of local, global and hybrid facial features to predict ages. Two variants …

Towards Rare Event And Anomaly Prediction In Manufacturing: Bridging Methodological Gaps In Industrial Applications, Chathurangi Shyalika, Renjith Prasad, Ruwan Wickramarachchi, Amit Sheth

Publications

Rare event prediction is critical in industrial applications, including real-world Industry 4.0 applications. These events, defined by their low occurrence frequency, are often difficult to predict due to the skewed data distribution, which complicates modeling and evaluation. In our research, we provide a comprehensive review of current approaches to rare event prediction across four key dimensions: rare event data, data processing techniques, algorithmic approaches, and evaluation methodologies [1]. By analyzing diverse datasets with multiple modalities, including numerical, image, text, and audio, we categorize the primary challenges and present the gaps in current research. Specifically, we present three novel research contributions …

Predicting Chaotic Systems With Quantum Echo-State Networks, Erik Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan

Publications

Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively …

Enhancement Of Ambient Air Quality Index Forecasting Using Optimized Ensemble Model, Vanitha M

Theses and Dissertations

Forecasting ambient air quality is essential for environmental sustainability and public health, especially in heavily populated regions such as China, India, and the United States where air pollution remains a serious concern. Traditional forecasting models often struggle to accurately represent air quality data because of its complex patterns and nonlinear interactions. To address these challenges and improve forecast performance, this research proposes a comprehensive strategy that integrates parallel heterogeneous ensemble modeling with Bayesian optimization.

The study begins with a seasonal machine learning–based imputation technique (SeasonalMLImpute) designed to handle missing data in meteorological and air quality parameters. This method is evaluated …