Computer Engineering Commons^™

Trisc: Low-Cost Design Of Trigonometric Functions With Quasi Stochastic Computing, Mehran Shoushtari Moghadam, M. Hassan Najafi

Faculty Scholarship

Low-cost and hardware-efficient design of trigonometric functions is challenging. Stochastic computing (SC), an emerging computing model processing random bit-streams, offers promising solutions for this problem. The existing implementations, however, often overlook the importance of the data converters necessary to generate the needed bit-streams. While recent advancements in SC bit-stream generators focus on basic arithmetic operations such as multiplication and addition, energy-efficient SC design of non-linear functions demands attention to both the computation circuit and the bit-stream generator. This work introduces TriSC, a novel approach for SC-based design of trigonometric functions enjoying state-of-the-art (SOTA) quasi-random bit-streams. Unlike SOTA SC designs of …

Nature Inspired Optimization For Spectrum Sensing And Allocation In Cognitive Radio Networks, Saravanan R

Theses and Dissertations

Cognitive radio (CR) refers to intelligent radio technology that scans its environment to optimize spectrum use and adjusts its parameters accordingly. It employs a communication system that is aware of its surroundings, including spectrum usage and availability. A key aspect of CR is identifying idle channels by analyzing traffic patterns using effective learning strategies.

However, CRNs face challenges such as cross-layer design issues, spectrum sensing errors, hidden node problems, and complex spectrum management. Spectrum sensing is critical for accessing unused radio spectrum while minimizing interference. Efficient sensing techniques must be cost-effective, fast, and capable of detecting weak primary signals. Although …

A Distributed Secure System For Threads Detection In Crowded Environments Based On Biometric Facial Expression, Nourhan Zayed, Hassan I. Sayed, Ghada F. El-Kabbany

Mechanical Engineering

Ensuring security in crowded areas poses a significant problem due to the dense population and the intricate task of monitoring their behaviors. This research presents a real-time security system that utilizes facial expression analysis to identify potential threats. The technology utilizes sophisticated facial recognition and emotion detection techniques to accurately recognize emotions such as wrath, fear, and anxiety. These feelings can serve as indicators of suspicious activities or potential security risks. The system utilizes a Convolutional Neural Network (CNN) to classify facial expressions and recognize objects. In addition, it integrates an SVM classifier trained on features derived from the combined …

Rad-Quasi-Prime Submodules, Rana Noorimajeed, Ghaleb Ahmed Hammood, Mahmood S. Fiadh, Lemya Abd Alameer Hadi

Iraqi Journal for Computer Science and Mathematics

Consider a left J-module I. The present study introduces the conception of rad-Quasi- Prime submodule, that serves as a dual popularization of both Quasi-Prime submodules and primary submodules. An apposite submodule A of an J-module named as rad- Quasi Prime if for all and with implies that either or . Numerous facts and characterizations that concerning are acquired.

Synthetic Network Creation And Visualization, Kaylee Sloat, Jeremy Evert

Student Research

The code in the repository, “synthetic_network_creation_and_visualization” is inspired by the foundational work presented in "NetProbe: A Fast and Scalable System for Fraud Detection in Online Auction Networks" by Shashank Pandit, Duen Horng Chau, Samuel Wang, and Christos Faloutsos.

Link to the original paper:https://kilthub.cmu.edu...

Impaired Speech Recognition Of Neurological Disorder Persons Using Machine Learning And Deep Learning Techniques, Vishnika Veni S

Theses and Dissertations

Speech Assistive Tools have emerged in recent years to support individuals with cognitive and neurological disorders in the field of assistive technology. People affected by neurological disorders such as autism, stroke, cerebral palsy, dysarthria, Parkinson’s disease, and brain injury often find it difficult to articulate desired sounds, resulting in impaired speech. As the population of impaired speakers continues to increase every year, there is a strong need to develop intelligent speech recognition systems for affected individuals. The primary objective of this research is to develop an Impaired Speech Recognition (ISR) system for the Tamil language. Word Recognition Accuracy (WRA) is …

You Want Me To Do What? But I Can’T Read That... Cataloging Soviet Russia Space Exploration Books, Katherine Loving, Phillip Fitzsimmons

Faculty Books & Book Chapters

The Al Harris Library received a donation of the archive of retired astronaut General Thomas P. Stafford. The continually growing collection includes over 200 boxes containing NASA reports, professional and personal pictures, videos, and more. Part of the collection is the approximately 700 books used for research by Stafford and his co-author Michael Cassutt to write the memoir We Have Capture: Tom Stafford and the Space Race (2002). Half of these books are a collection of Soviet-era Russia (USSR) space program books and periodicals written in Russian. Michael Cassutt has characterized the books as the most comprehensive collection about the …

An Overview Of Ancillary Services Provided By Vehicle-To-Grid Systems, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

Vehicle-to-Grid (V2G) systems are emerging as a pivotal technology in modern power systems, offering a range of ancillary services that enhance the stability and reliability of power systems. This paper provides an overview of the key ancillary services provided by V2G systems, highlighting their role in grid modernization. Technical challenges, economic implications, and policy considerations associated with the deployment of V2G systems are explored to assess their potential impact on advancing a more resilient and sustainable energy infrastructure.

Ontology Design Metapattern For Relationtype Role Composition, Utkarshani Jaimini, Ruwan Wickramarachchi, Cory Henson, Amit Sheth

Publications

RelationType is a metapattern that specifies a property in a knowledge graph that directly links the head of a triple with the type of the tail. This metapattern is useful for knowledge graph link prediction tasks, specifically when one wants to predict the type of a linked entity rather than the entity instance itself. The RelationType metapattern serves as a template for future extensions of an ontology with more fine-grained domain information.

Visual Causal Question And Answering With Knowledge Graph Link Prediction, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The ability to answer causal questions is important for any system that requires robust scene under- standing. In this demonstration, we develop a prototype system that leverages our causal link prediction framework, CausalLP. CausalLP framework uses a visual causal knowledge graph and associated knowledge graph embedding for two visual causal question and answering tasks- (i) causal explanation and (ii) causal prediction. In the live demonstration sessions, the participants will be invited to test the efficiency and effectiveness of the system for visual causal question and answering.

Safety-Centric Analysis Of Grounding Systems For Substations In Distribution Grids, Fazel Mohammadi, Mahmood Mirhashemi

Electrical & Computer Engineering and Computer Science Faculty Publications

The safety of grounding systems for substations in distribution grids is paramount to ensuring operational reliability, protecting personnel and equipment, and maintaining the stability of distribution grids while complying with regulatory standards. This paper explores essential safety aspects of grounding systems, including fault current handling strategies, the interdependence between protective devices and grounding systems, and maintenance practices. The integration of grounding systems design with overall substation layout and design considerations by focusing on mitigating Ground Potential Rise (GPR) and optimizing bonding techniques, is examined. Additionally, advanced techniques, such as high-frequency grounding and Transient Ground Potential Rise (TGPR) management, are presented …

Causal Neuro-Symbolic Ai For Root Cause Analysis In Smart Manufacturing, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

Root cause analysis is the process of investigating the cause of a failure and providing measures to prevent future failures. It is an active area of research due to the complexities in manufacturing production lines and the vast amount of data that requires manual inspection. We present a combined approach of causal neuro-symbolic AI for root cause analysis to identify failures in smart manufacturing production lines. We have used data from an industry-grade rocket assembly line and a simulation package to demonstrate the effectiveness and relevance of our approach.

Causal Knowledge Graph For Scene Understanding In Autonomous Driving, Utkarshani Jaimini, Cory Henson, Amit Sheth

Publications

The current approaches to autonomous driving focus on learning from observation or simulated data. These approaches are based on correlations rather than causation. For safety-critical applications, like autonomous driving, it’s important to represent causal dependencies among variables in addition to the domain knowledge expressed in a knowledge graph. This will allow for a better understanding of causation during scenarios that have not been observed, such as malfunctions or accidents. The causal knowledge graph, coupled with domain knowledge, demonstrates how autonomous driving scenes can be represented, learned, and explained using counterfactual and intervention reasoning to infer and understand the behavior of …

Bridging Human And Machine Intelligence: Reverse-Engineering Radiologist Intentions For Clinical Trust And Adoption, Akash Awasthi, Ngan Le, Zhigang Deng, Rishi Agrawal, Carol C. Hu, Hien Van Nguyen

Computer Science and Computer Engineering Faculty Publications and Presentations

In the rapidly evolving landscape of medical imaging, the integration of artificial intelligence (AI) with clinical expertise offers unprecedented opportunities to enhance diagnostic precision and accuracy. Yet, the "black box" nature of AI models often limits their integration into clinical practice, where transparency and interpretability are important. This paper presents a novel system leveraging the Large Multimodal Model (LMM) to bridge the gap between AI predictions and the cognitive processes of radiologists. This system consists of two core modules, Temporally Grounded Intention Detection (TGID) and Region Extraction (RE). The TGID module predicts the radiologist's intentions by analyzing eye gaze fixation …

Analysis Of Multivariable Sensor Responses To Multi-Analyte Gas Samples In The Presence Of Interferents And Humidity, Sakin Sarwar Satter, Florian Bender, Nicholas Post, Antonio J. Ricco, Fabien Josse

Electrical and Computer Engineering Faculty Research and Publications

This work presents an adaptive sensor signal-processing approach to enable quantification, using a single gas sensor or a small sensor array, of multianalyte mixtures of aromatic hydrocarbons in the presence of various interferents and humidity for environmental-monitoring applications. Dynamic sensor responses are analyzed by extracting multivariable sensing parameters to provide necessary sensitivity and selectivity. This is achieved by integrating the Levenberg–Marquardt-modified, exponentially weighted, recursive-least-squares-estimation (LM-modified EW-RLSE) algorithm and principal-component analysis (PCA). Achieving measured detection limits as low as 3 μg/L (≤1 ppm by volume) for 6 target analytes, the system exhibits excellent PCA cluster separation for all analytes in the …

Resource Harvesting For Parallel Functions In Serverless Workflows, Peiman Fotouhi

LSU Master's Theses

In the rapidly evolving landscape of cloud computing, serverless architectures have gained attention for their scalability and cost-effectiveness. This thesis aims to introduce a novel approach to maximize resource utilization in serverless environments through the concept of harvesting idle resources within Directed Acyclic Graph (DAG)-based workloads. Our proposed solution targets resource harvesting at parallel stages by utilizing Machine Learning models to accurately harvest or accelerate serverless functions. Additionally, we present a scheduling algorithm specifically designed to address the unique requirements of DAG workloads in cloud environments.

The framework leverages dynamic resource allocation techniques to identify and exploit idle resources within …

Performance Analysis Of C++ Parallel Algorithms In Hpx, Srinivas Yadav Singanaboina

LSU Master's Theses

The exponential growth in computational power and the increasing demand for high-performance applications have driven the need for greater parallel efficiency. Over the years, the number of cores in consumer-level CPUs and high-performance computing (HPC) systems has grown significantly. In response, numerous parallel programming li- braries have been developed. Each of these libraries offers unique mechanisms to enhance parallel performance. In this paper, we investigate the performance of five such paral- lel programming backends: C++ std::execution::par, OpenMP, TBB, Taskflow, and HPX. We evaluate these libraries using two sets of benchmarks. The first set focuses on standard C++ STL algorithms, including …

Detecting Data Poisoning Attacks In Federated Learning For Healthcare Applications Using Deep Learning, Mohammed Aljanabi, Sahar Yousif Mohammed, Alaa Hamza Omran

Iraqi Journal for Computer Science and Mathematics

This work introduces a new approach to protecting the data in the healthcare applications of federated learning based on the classification of skin cancer. The recommended solution established and prevents the data poisoning attacks by using deep learning and CNN architectures namely VGG16. In a federated learning system which comprises of ten healthcare facilities, the approach enables the training of models in a collaborative way without compromising the medical data or the patients’ information. Data is meticulously prepared and preprocessed using the Skin Cancer MNIST: According to the HAM10000 dataset. As for the federated learning approach, VGG16’s feature extraction capability …

Prototyping Interactive Tactile Digital Logic Simulations: A Hybrid Approach, Logan Bateman

MS in Computer Science Project Reports

Tactile exhibits are common in museums and on the walls of university halls. However, few (if any) tools exist for creating tactile exhibits for teaching digital logic or computing concepts. This project implemented a framework for creating tactile digital logic simulation exhibits, with a focus on rapid prototyping and distributed architecture. Prototyping allows for fast iteration, with the ability to simulate unlimited hardware components such as buttons, light emitting diodes (LEDs), and other input or output devices. Through the abstraction of implementations and a distributed communication protocol, switching to real hardware is seamless and works in tandem with simulated hardware. …

Enhanced Shoulder-Surfing Cued Recall Graphical Password System: Sequential Passpoint, Titus D. Fofung

Cybersecurity Graduate Research Symposium

During the past two decades, many graphical passwords have been used widely as an alternative to text-based passwords. However, most graphical password systems are plagued by shoulder-surfing problems, usability, and remembering capability. This study proposed a new graphical password called SPP (Sequential PassPoint), allowing users to remember three click-points on two images in specified order and image order. When the image order changes, the click order is reversed. Two decoy images for three random clicks were introduced to enhance the security of SPP. The proposed SPP system was validated both theoretically and empirically

Experimental Study To Assess The Role Of Environment And Device Type On The Success Of Social Engineering Attacks: The Case Of Judgment Errors, Tommy Pollock

Cybersecurity Graduate Research Symposium

No abstract provided.

Assessing Organizational Investments In Cybersecurity And Financial Performance Before And After Data Breach Incidents Of Cloud Saas Platforms, Munther B. Ghazawneh

Cybersecurity Graduate Research Symposium

No abstract provided.

Examining Consumers' Selective Information Privacy Disclosure Behaviors In An Organization's Secure E-Commerce Systems, Patrick I. Offor

Cybersecurity Graduate Research Symposium

No abstract provided.

Dcai: The 4th International Workshop On Data-Centric Ai, Yanjie Fu, Kunpeng Liu, Dongjie Wang

Computer Science Faculty Publications and Presentations

Machine learning traditionally emphasizes developing models for given datasets, but real-world data is often messy, making model improvement insufficient for enhancing performance. Data-Centric AI (DCAI) is an emerging field that systematically improves datasets, leading to significant practical ML advancements. While experienced data scientists have manually refined datasets through trial-and-error and intuition, DCAI approaches data enhancement as a systematic engineering discipline. DCAI represents a shift from focusing on models to the underlying data used for training and evaluation. Despite the dominance of common model architectures and predictable scaling rules, building and using datasets remain labor-intensive and costly, lacking infrastructure and best …

Interpreting Black-Box Time Series Classifiers Using Parameterised Event Primitives, Ephrem Tibebe Mekonnen, Luca Longo, Pierpaolo Dondio

Conference papers

Amidst the remarkable performance of deep learning models in time series classification, there is a pressing demand for methods that unveil their prediction rationale. Existing feature importance techniques often neglect the temporal nature of time series data, focusing solely on segment importance. Addressing this gap, this paper introduces a local model-agnostic method akin to LIME, which generates neighbouring samples by randomly perturbing segments of the original instance. Subsequently, weights are computed for each neighbouring instance based on its distance from the original, elucidating its influence. Parameterised event primitives (PEPs) are then extracted from these perturbed samples, encompassing increasing and decreasing …

Three-Dimensional Outdoor Object Detection In Quadrupedal Robots For Surveillance Navigations, Muhammad Hassan Tanveer, Zainab Fatima, Hira Mariam, Tanazzah Rehman, Razvan Cristian Voicu

Faculty Articles

Quadrupedal robots are confronted with the intricate challenge of navigating dynamic environments fraught with diverse and unpredictable scenarios. Effectively identifying and responding to obstacles is paramount for ensuring safe and reliable navigation. This paper introduces a pioneering method for 3D object detection, termed viewpoint feature histograms, which leverages the established paradigm of 2D detection in projection. By translating 2D bounding boxes into 3D object proposals, this approach not only enables the reuse of existing 2D detectors but also significantly increases the performance with less computation required, allowing for real-time detection. Our method is versatile, targeting both bird’s eye view objects …

A Plugin-Based Unreal Engine Adapter For Hla-Based Distributed Simulation, Mei Yang, Peng Wang

Journal of System Simulation

Abstract: With the wide application of game engine-based simulation in transportation, military and other fields, the demand for interoperability between game engine and traditional simulations is becoming increasingly strong. For the HLA-based integration of Unreal Engine and the traditional simulations, a plugin-based Unreal Engine adapter for distributed simulation is designed, which enables the rapid development of Unreal Engine federate and the efficient integration. The simulation shows the feasibility of the plugin-based Unreal Engine adapter.

Research On Sequential Design Methods For Satellite Combat Simulation Tests, Yanlin Wang, Zhijun Cheng, Zichen Wang, Jian Zhong

Journal of System Simulation

Abstract: Aiming at the problem that satellite monitoring mission simulation tests cannot take into account the number of sample points and model accuracy in the complex test space, a hybrid sequential test design method for satellite simulation tests based on sample density and local nonlinearity is proposed. Voronoi division is used to describe the density of discrete point distribution, and the nonlinearity is measured with the help of Taylor expansion and sample point neighborhood gradient information. The two are combined to calculate the hybrid metrics, and the sample points are ranked and new ones are added until the stopping criterion …

Improving Nsga-Iii Algorithm For Solving High-Dimensional Many-Objective Green Flexible Job Shop Scheduling Problem, Yigang Xu, Yong Chen, Chen Wang, Yunxian Peng

Journal of System Simulation

Abstract: Aiming at the poor initial solution quality and low local search efficiency of NSGA-III in solving the many-objective flexible job shop scheduling model, an improved NSGA-III (NSGA-III-TV) is proposed. Based on MSOS encoding, the different mixed initialization strategies are adopted for OS and MS chromosomes to improve the quality of initial solutions. Based on the critical path, an improved N6 neighborhood structure is used for neighborhood search, which effectively reduce the completion time and reducing search randomness. Three effective mutation operators are employed to expand the search space and improve the convergence capability in the later stages. Test results …

Multi-Objective Energy-Efficient No-Wait Flow Shop Scheduling Based On Hybrid Discrete State Transition Algorithm, Cong Wang, Jiaying Yu, Hongli Zhang

Journal of System Simulation

Abstract: A hybrid discrete state transition algorithm (HDSTA) is designed to solve the energyefficient no-wait flow shop scheduling problem (EENWFSP) minimizing makespan and total energy consumption. According to the characteristics of the problem, the coding method of job sequence and speed matrix is designed, and the heuristic algorithm is used to obtain the high-quality initial solution. According to the properties of EENWFSP, solving and allocating four discrete operators. The swap, shift and symmetry operators are embedded in secondary state transition are used for job sequence optimization, and the substitute operators are used for machine speed optimization. The speed substitute strategy …