Computer Engineering Commons^™

A Review Of Intelligent Generation Of Combat Simulation Scenarios, Zhiming Dong, Zhongqi Hu, Zhaoyang Liu, Heyang Zhou

Journal of System Simulation

Abstract: In order to improve the efficiency of combat simulation, this paper provided a theoretical reference for the research on the intelligent generation of combat simulation scenarios. It systematically reviewed the intelligent generation methods of combat simulation scenarios based on large language models (LLMs). It began by introducing the basic content of combat simulation scenarios, analyzed the shortcomings of current mainstream scenario generation methods, and discussed how to leverage LLMs to address these issues. Next, it outlined the application paradigms and key supporting technologies for the intelligent generation of combat simulation scenarios based on LLMs. Finally, it pointed out the …

Research On Requirements And Methods For Intelligent Assessment Of Simulation Credibility, Bingheng Wang, Tingrui Liu, Fan Yang, Huan Zhang, Wei Li, Ping Ma, Ming Yang

Journal of System Simulation

Abstract: The accuracy of simulations in representing real-world systems is a critical concern for users. Simulation credibility assessment ensures trustworthiness by evaluating the correctness and effectiveness of simulations to meet application requirements. As simulation technologies are widely adopted, and new simulation paradigms emerge, traditional assessment methods are increasingly showing limitations in their dependence on experts, data processing capabilities, and assessment efficiency. This paper systematically reviewed the research demands, current progress, new technologies, and future trends of intelligent simulation credibility assessment. Based on the simulation credibility assessment process and problem analysis, the requirements for intelligent credibility assessment were discussed. Intelligent technologies …

Optimization And Simulation Of Adaptive Production Scheduling Based On Hybrid Decision-Making Mechanism, Zhicheng Ji, Zhen Quan, Yan Wang

Journal of System Simulation

Abstract: To optimize flexible production scheduling with the objectives of the longest makespan, mean tardiness, and bottleneck machine processing load rate, a hybrid decision-making mechanism scheduling algorithm was proposed based on the decision complexity and constraint characteristics of machine assignment and task sequencing. The algorithm adopted a two-dimensional chromosome to encode machine assignment and a heuristic rule to evaluate task sequencing priority, enhancing the adaptability of the method to decision-making optimization. In order to further improve the performance of the proposed scheduling method, an adaptive rule strategy was designed based on the distribution of processing time required for waiting scheduling …

Research On Simulation Training Technology For Special Vehicle Command Based On Gesture Behavior Cognition Interaction, Xiangyang Li, Zhili Zhang, Rui Wang, Xiao Wang, Cheng Chi

Journal of System Simulation

Abstract: To overcome the practical shortcomings in the driving command training of special vehicles, a simulation training technology framework based on gesture behavior cognition and command interaction was proposed. A somatosensory interactive motion capture device and human skeleton model were used to conduct the real-time dynamic recognition and spatial coordinate transformation of the gesture actions of the command training personnel. The median filtering method was applied to eliminate the abrupt data and random noise. The spatial coordinates were processed consistently by using the human skeleton centralization and normalization method. Based on the command gesture action behavior model library and the …

Combat-Oriented Comprehensive Simulation And Verification Technology For Equipment System Rms, Yue Zhang, Wenliang Zhang, Qiang Feng, Xing Guo, Yi Ren, Zili Wang

Journal of System Simulation

Abstract:Existing reliability maintainability supportability (RMS) simulation and verification methods for equipment systems are typically conducted under standard conditions and suffer from weak combat environments and task modeling capabilities. To address this limitation, a multi-agent RMS simulation and verification framework was proposed. Key breakthroughs included agent modeling techniques for complex environments and variable tasks, interaction mechanisms among environmental agents, task agents, equipment, and support systems, and a simulation-based comprehensive RMS evaluation method. Case studies demonstrate that the proposed method effectively models complex environments and variable tasks, supports combat-oriented simulation and verification and design scheme evaluation, and meets combat-ready development requirements.

Multi-Scenario Multi-Satellite Mission Planning Method Based On Adaptive Large Neighborhood Search, Xiutian Li, Ling Wang, Yingwu Chen, Lining Xing, Yingguo Chen

Journal of System Simulation

Abstract: To further improve the execution efficiency of remote sensing satellites, an integrated optimization framework combining adaptive large neighborhood search (ALNS) and a constraint programming-boolean satisfiability problem (CP-SAT) solver monitor was proposed, addressing the challenges of complex constraints, dynamic scale, and resource heterogeneity in multi-scenario multi-satellite mission planning. A unified multi-objective mixed-integer programming model was established, coupling heterogeneous constraints of point targets and area tasks. A time-domain rolling mechanism dynamically decomposed the problem scale, and a priority screening strategy enhanced the search efficiency of ALNS. Solution feasibility was verified in real time through the CP-SAT monitor. Results show that compared …

Dynamic Data Driven Simulation Based On Macro-Microscopic Hierarchical Simulation Models, Xu Xie, Yuqing Ma

Journal of System Simulation

Abstract:This paper proposed a dynamic data driven simulation approach based on macro-microscopic hierarchical simulation models. This approach enabled the measurement data from the real system to affect the macroscopic simulation and microscopic simulation in sequence and made the two simulations evolve together so that it could provide decision makers with the state evolution prediction of the real system at the macroscopic level to assist decision making and provide a microscopic testbed similar to the real system, on which decision makers could deduce and evaluate their strategies. This paper established a formal description of the approach and designed a data …

Research On High-Performance Optimization Methods For Fastdds In Heterogeneous Real-Time Simulation, Congping Liu, Wei Song, Jian Fang, Fei Liu

Journal of System Simulation

Abstract:FastDDS faces limitations under high-frequency data streams, such as lock contention, performance overhead from frequent context switching, and configuration complexity of multiple nodes under strict real-time constraints, which affect the experimental efficiency. This paper proposed a performance optimization method based on a batch-scalable circular queue (BSCQ). The approach replaced the traditional mutex mechanism with a lock-free algorithm to reduce lock contention and avoid deadlocks, while batch processing improved data locality, cache hit rates, and memory utilization, effectively reducing data transmission delay and improving system throughput. Hazard pointers were introduced to ensure safe memory management during batch processing and eliminate …

Reflections On Innovative Approaches To Autonomous Simulation Software, Feng Tian

Journal of System Simulation

Abstract: Given China's weak foundation in simulation software, simply replicating the development paths of these global leading companies offers limited potential for leapfrog development. Based on an analysis of pitfalls in the independent innovation of domestic simulation software, this paper proposed and elaborated on six strategic approaches: avoiding established paths, aligning with national realities, pursuing extreme performance, leveraging special needs to advance technology, building new systems from the ground up, and embracing artificial intelligence (AI)-native architectures. These strategies aim to provide new perspectives for the independent innovation and development of domestic simulation software.

Enhanced Restoration Of Covid-19 Ct Scan Images Utilizing Advanced Wiener Filtering Techniques, Warqaa Shaher Alazawee, Marwa Subhi Ibrahim, Raghda Salam Al Mahdawi, Ali Albu-Rghaif, Ahmed Sabri Altaie

Iraqi Journal for Computer Science and Mathematics

COVID-19, caused by the SARS-CoV-2 virus, was declared a global pandemic by the World Health Organization (WHO) and rapidly spread worldwide from late 2019. While Reverse Transcription Polymerase Chain Reaction (RT-PCR) is the primary diagnostic tool, its sensitivity ranges from only 60% to 70%, leading to false negatives. Computed Tomography (CT) imaging has emerged as a valuable alternative for accurate diagnosis; however, the quality of CT images is often degraded by motion-induced blur and additive noise, particularly in children, individuals with mental health conditions, or those with phobias of CT scans. This study aims to enhance COVID-19 CT image quality …

Motionfusion: A Robust Ensemble Learning Framework For Accurate Sensor-Based Human Activity Recognition, Hussein K. Almulla, Hussam J. Mohammed, Alaa S. Al-Waisy, Shumoos Al-Fahdawi, Ahmed Adnan Had, Bourair Al-Attar

Iraqi Journal for Computer Science and Mathematics

Human activity Recognition (HAR) has emerged as an important research area due to its potential applications in health, sport, and recreation. The widespread availability of smartphone sensors has facilitated data collection for HAR systems. Although machine learning and deep learning models have proven to be effective in detecting human activity from sensor data, their performance may be limited, this study proposes MotionFusion which is an ensemble learning model to increase HAR accuracy utilizing accelerometer and gyroscope data from a smartphone. By combining Histogram-Based Gradient Boosting, Random Forest, and Extra Trees models with a Support Vector Machine classifier and using feature …

Retracted: A Quantum Convolutional Neural Network Approach For Early And Accurate Diagnosis Of Parkinson's Disease, Aiesha Mahmoud Ibrahim, Mazin Abed Mohammed, Omar Al-Boridi

Iraqi Journal for Computer Science and Mathematics

Parkinson's disease (PD) is a progressive neurological disorder that primarily affects individuals over the age of 55. It is characterized by a range of motor and non-motor symptoms that can significantly impact various aspects of daily life. Despite notable advancements in medical science, there is currently no permanent cure or definitive treatment for PD. This therapeutic gap underscores the critical importance of early diagnosis, which remains a major focus of ongoing research. Due to the disease's gradual progression, PD symptoms may take years to fully develop, making early detection essential for improving patient outcomes and quality of life. Moreover, the …

Retracted: Solving Time-Fractional Nonlinear Variable-Order Delay Pdes Using Feedforward Neural Networks, Hala S. Alruhaili, Adel S. Hussain, Abdullah M. S. Ajlouni, Funda Türk, Emad A. Az-Zo’Bi, Mohammad A. Tashtoush

Iraqi Journal for Computer Science and Mathematics

This study presents an innovative application of Feedforward Neural Networks ‘FNNs’ to solve Variable-Order Fractional Partial Differential Equations ‘VO-FPDEs’ with time delays. Utilizing the Caputo definition, the variable-order fractional derivatives are approximated in terms of integer-order derivatives. The problem is reformulated as a system of partial differential equations with delay terms, which is then addressed using ‘FNNs’ to achieve explicit approximate solutions. Comprehensive error and convergence analyses validate the method’s precision and reliability. The effectiveness of the proposed approach is highlighted through numerical examples, with graphical and tabular representations showcasing minimal absolute errors and robust convergence. These results demonstrate the …

A Predictive Framework For Early Detection And Personalised Monitoring Of Parkinson’S Disease Using Artificial Intelligence And Large Language Models, Priyadharshini S

Theses and Dissertations

Parkinson’s Disease (PD) is a multifaceted and progressive neurodegenerative disorder that presents a spectrum of motor and non-motor symptoms. Early and accurate diagnosis is essential for effective disease management and improved patient outcomes, yet remains clinically challenging due to symptom overlap and diagnostic limitations. This thesis proposes a comprehensive and interpretable artificial intelligence (AI)-driven diagnostic framework that aims to transform the early detection, personalised monitoring, and treatment recommendation process for PD. The proposed solution integrates deep learning, radiomics, evolutionary optimisation, and large language models (LLMs), ensuring a highly accurate and clinically adaptable system.

The research begins by analysing T2-weighted 3D …

Towards Leveraging Social Media Data For Fostering Collaborations Among Non-Profits, Monazil Chowdhury

LSU Doctoral Dissertations

Nonprofit organizations serve a crucial role in tackling a wide range of significant social, environmental, and economic issues. But it is often hard to get a clear picture of their work because their information is spread out and it is difficult to see how they are collaborating. To address this issue we developed a web-based tool to collect scattered data—from a variety of sources, such as the IRS, social media, and the Census, into one easy-to-use resource. The tool begins by taking IRS records and geocoding each nonprofit’s physical address With its coordinates. It then retrieves census tract information from …

Security Analysis Of Encrypted Mempool, Prabal Das

Master’s Dissertations

With the rapid growth of Decentralized Finance (DeFi), the challenge of Maximum Extractable Value (MEV) has become increasingly significant-particularly on Ethereum. MEV allows malicious actors to manipulate transaction order within blocks, enabling exploitative strategies such as frontrunning and sandwich attacks. In response, recent research has proposed encrypted mempools, which conceal trans- action content until after ordering is finalized, thereby reducing the exploitable surface for MEV. This thesis investigates encrypted mempool, specifically the Shutter protocol, a threshold encryption-based approach designed to mitigate MEV by hiding transac- tion contents during the mempool phase. We analyze its core architecture, under- lying cryptographic mechanisms, …

Advancing Eye-Gaze Writing Systems With Computer Vision, And Dynamic Text Suggestions, Walid Abdallah Shobaki

Theses and Dissertations

Eye gaze writing, a novel interaction modality, has the potential to revolutionize communication for individuals with limited mobility. In our research, we investigated the deep learning algorithms efficiency for real-time eye gaze writing. We have compared many algorithms' performance in many computer vision areas, such as object detection in which we used first YOLOv8, the second algorithm SSD, and the third algorithm is Faster R-CNN, the second computer vision area is the image segmentation in which we used DeepLab and U-Net, and the last computer vision area is self-supervised learning we have used SimCLR algorithm. By evaluating these models on …

Digital Twins For Circular Economy Optimization: A Framework For Sustainable Engineering Systems, Shubham Gupta

Harrisburg University Other Works

This paper introduces sustainable engineering systems built using digital twin technology and circular economy principles. This research presents a framework for monitoring, modeling, and making decisions in real timusing virtual replicas of physical products, processes, and systems in product lifecycles. A digital twin was used to show that through a digital twin, waste was reduced by 27%, energy consumption was reduced by 32%, and the resource recovery rate increased to 45%. The proposed approach under the framework employs various machine learning algorithms, IoT sensor networks, and advanced data analytics to support closed-loop flows of materials. The results show how digital …

Design & Development Of Efficient Biometric Authentication And Key Agreement Schemes For Wireless Body Area Network, Aarthi S

Theses and Dissertations

Wireless Body Area Networks (WBANs) play a vital role in continuous health monitoring, where sensitive biometric and physiological data must be protected from privacy breaches and emerging quantum-based threats. Conventional security mechanisms are often inadequate due to resource constraints, scalability issues, and vulnerability to advanced attacks. To address these challenges, this research proposes a lightweight, scalable, and future-proof security framework tailored for WBAN applications, focusing on secure communication, privacy preservation, and quantum resilience.

An anonymous Certificate-Based Signcryption–Mutual Authentication and Key Agreement (CBS-MAKE) protocol is introduced to secure extra-body communications while ensuring patient anonymity. The protocol integrates Elliptic Curve Cryptography with …

Low Entropy Side-Channel Secure Hardware Implementations, Jhelum Dhar

Master’s Dissertations

The demand for symmetric-key cryptography implemented in hardware is growing due to the increasing need for faster, more efficient, and secure encryption in small devices. However, implementing block ciphers in hardware that are side-channel secure remains a challenging goal. This holds true because there exist sophisticated but well-studied attacks such as Differential Power Analysis, which uses the correlation between power consumption of a device and the information on it to allow attackers with physical access to the cryptographic device to get information about secret data. Masking is one of the techniques that is used to provide security against sidechannel attacks. …

Book Review: Building A God, Carl Lee Tolbert

The Journal of Values-Based Leadership

No abstract provided.

Explicit Bounds And Parallel Algorithms For Counting Multiply Gleeful Numbers, Sara Moore, Jonathan P. Sorenson

Computer Science and Software Engineering

Let k ≥ 1 be an integer. A positive integer n is k-\textit{gleeful} if n can be represented as the sum of kth powers of consecutive primes. For example, 35=2³+3³ is a 3-gleeful number, and 195=5²+7²+11² is 2-gleeful. In this paper, we present some new results on k-gleeful numbers for k > 1.

First, we extend previous analytical work. For given values of x and k, we give explicit upper and lower bounds on the number of k-gleeful representations of integers n ≤ x.

Second, we describe and analyze two new, efficient parallel …

Ti-Ulpcs: Threshold-Issuance - Un-Linkable Policy Compliant Signatures, Kiran Deep Ghosh

Master’s Dissertations

Digital signatures, as a strong cryptographic primitive, ensure the authenticity and integrity of signed messages. On one hand, no one can forge a verifiable signature without knowing the secret key, on the other hand, any correctly formed signature is always verifiable under the public key of the signer. Besides signing digital data, they serve as foundational components in more advanced cryptographic systems, including blind signatures, group signatures, direct anonymous attestation, e-cash, e-voting protocols, adaptive oblivious transfer, anonymous credential schemes, Policy-Compliant Signatures, etc. Policy-Compliant Signatures (PCS) enable the enforcement of joint policies between the signer and the verifier. A signature of …

Zkipv: Zero-Knowledge Proofs For Image Provenance Verification, Bibek Ghosh

Master’s Dissertations

Recent advances in generative AI have significantly improved the ability to create photorealistic synthetic images, including so-called deepfakes, raising concerns about misinformation and the erosion of trust in digital media. Ensuring the integrity and authenticity of images, especially in sensitive domains like journalism, is thus increasingly critical. Existing solutions such as the C2PA (Content Provenance and Authenticity) framework provide origin verification through cameragenerated digital signatures, but fail to account for image modifications that invalidate these signatures. To address this limitation, we propose a zero-knowledge approach to verifiable image editing that preserves both integrity and privacy. This system, ZK-IPV, introduces a …

Efficient Simd Based Implementation Of Xoodyak, Soham Biswas

Master’s Dissertations

Modern computing devices—particularly in the domains of the Internet of Things (IoT), mobile computing, and embedded systems—often operate under severe resource constraints in terms of processing power, memory (RAM/ROM), bandwidth, and battery life. Devices such as IoT sensors, smart cards, medical implants, RFID tags, and wearable systems typically rely on low-power hardware, including 8-bit microcontrollers with only a few kilobytes of memory. Conventional cryptographic algorithms are frequently unsuitable for such environments, as they may consume excessive power, introduce unacceptable latency, or fail to execute altogether. Lightweight cryptography addresses these challenges by providing cryptographic primitives specifically designed to operate efficiently on …

The Monodromy Leak For A Generalized Montgomery Ladder, Arani Raychaudhuri

Master’s Dissertations

The Diffie-Hellman key exchange protocol using elliptic curves is the most wide-spread approach to the establishment of a secure internet connection. As an important subroutine, Alice and Bob need to perform multiplications of elliptic curve points by large scalars. The textbook method for scalar multiplication is the double-and-add algorithm. For the sake of efficiency, one usually performs x-coordinate only arithmetic using projective coordinates, and doubling-and-adding is done using the Montgomery ladder. The advantage of using projective coordinates is that this avoids costly field inversions at each iteration. However, when Alice (say) uses the double-and-add algorithm for computing her public key …

Design Of A Simplified Biomimetic Autonomous Entertainment Robot, Jessica E. Morris, Shanny May Ruiz, Morgan Snyder, Hannah Guild, Gabriel Cardona-Tous

Graduate Scholarship and Creative Works

The advancement of technology has paved the way for robots with the purpose of entertainment to become more popular. However, the exploration of biomimetic actions as a factor of entertainment is majorly limited to the mimicry of young domesticated animals such as puppies and kittens, with some notable exceptions. However, there is a wide range of possibilities for robotics that mimic animals marketed as pets, outside of the common examples. This work seeks to explore the opportunity for autonomous systems with entertainment driven and expressive actions in the family of Testudines (turtles and tortoises), an undomesticated species, but still often …

Edge-Streamer: A Lightweight, Self-Supervised Event Segmentation Model, Lucas Miller

USF Tampa Graduate Theses and Dissertations

Event segmentation is the practice of autonomously detecting the boundaries of semantically connected sequences of actions within a video. It is connected to many areas of computer vision, including action recognition and event understanding. Nearly all current work requires the entire video to be stored in memory to be processed in multiple passes. This takes up valuable resources, increases processing time, prevents the use of low-power, low-memory devices, and precludes long-form content and live videos from undergoing event segmentation.

This thesis introduces EDGE-STREAMER, a real-time, lightweight, self-supervised, transformer architecture capable of performing event segmentation in a single pass. EDGE-STREAMER uses …

Tripping The Telematic Fantastic: Adventures In Presenting Telematic Musicing At In-Person Conferences In 2024, Tom Zlabinger

Journal of Network Music and Arts

This article documents my challenges, successes, and what I learned while demonstrating what I describe as telematic musicing at six academic conferences in the U.S., Scotland, Finland, and Lithuania in 2024. Through personal narrative, autoethnography, and audience reactions, I share observations and conclusions I drew as a result of demoing telematic musicing to different audiences. I outline the technological and attitudinal challenges I encountered at the conferences and assess whether I was able to resolve them. I also discuss the need to strike and maintain a balance between hardware, software, and what Peter Neumann calls “peopleware.” Finally, I reflect on …

Transmission: Works Of Sarah Rose Weaver And Collaborations (2022–2024), Sarah Rose Weaver

Journal of Network Music and Arts

The Transmission Series took place between 2022 and 2024, primarily in a virtual network music setting. The series consists of my compositions and collaborations for solo, chamber, and large ensemble performance, building on my works and essays since 1998 that engage in-person, hybrid, and virtual spaces. This essay details the artistic and technological strategies employed in the Transmission Series, including audio, video, and score excerpts. The technologies discussed include networked audio and video, along with streaming and recording methods. The works featured in the discussion are “Transcendence Transmission” (2022) for large ensemble, “Sub Way” (2022) for chamber ensemble, “Here Mirror” …