Computer Engineering Commons^™

Executive Order On The Safe, Secure, And Trustworthy Development And Use Of Artificial Intelligence, Joseph R. Biden

Copyright, Fair Use, Scholarly Communication, etc.

Section 1. Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative, and secure. At the same time, irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security. Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.

My Administration places the highest urgency …

A Precise Attention Tracking System Based On Computer Vision, Jiyuan Liu, Hanwen Qi, Zhicheng Liu, Minrui Fei, Kun Zhang

Journal of System Simulation

Abstract: A precise attention tracking system based on machine vision is designed to address the difficulty in studying students' attention allocation. The system includes an image capture device and an accurate attention tracking algorithm. The image capture device can capture the clearer ocular images. The pupil center localization algorithm replaces VGG16 with lightweight MobileNetv3 and uses twostage feature fusion and center keypoint prediction techniques to improve the speed and accuracy. The algorithm achieves a speed of up to 36 frames/s and 97.42% accuracy. The gaze tracking algorithm compensates for the head movements to achieve the meticulous gaze tracking. An interactive …

Modeling And Analysis On Scattering Characteristics Automatic Driving Radar Bands In Rainy Environment, Mengfan Zou, Xiaoyu He

Journal of System Simulation

Abstract: The operating frequency band of modern communication and radar systems has extended to millimeter wave and terahertz frequency band, and the analysis on propagation characteristics of electromagnetic signals in rainy environment is important. A calculation model through Mie scattering theory is built to simulate the attenuation and the scattering of electromagnetic signals in rainy environments. Different types of raindrop size distribution function are adopted to analyze the propagation attenuation under different rainfall of frequencies spanning from 1 GHz to 1 THz. Experimental results are compared with international telecommunication union (ITU) half-empirical model to verify the validation of the model. …

Integrated Scheduling Simulation Based On Improved Moth Flame Optimizer, Tianrui Zhang, Huiyuan Niu, Wei Xie

Journal of System Simulation

Abstract: Aiming at the rising cost of manufacturing enterprises, a mathematical programming model of integrated scheduling of production and transportation is established, and a double adaptive weights for moth flame optimizer(DAWMFO) is proposed. A double adaptive weight mechanism is proposed. The spiral function is used to update the population, which improves the convergence speed and accuracy of the algorithm. The benchmark function is tested by the improved algorithm. The results show that the improved algorithm can converge quickly and not easily fall into local optimum. Compared with other algorithms, the optimization ability is also improved. Through the simulation experiment on …

Simulation And Research Of Manipulator Motion Strategy Based On Adaptive Dynamic Programming, Ming Li, Qun Xu, Yan Wang, Zhicheng Ji

Journal of System Simulation

Abstract: Aiming at the difficulty of manipulator to realize high-precision motion tracking in complex and harsh environment, a strategy method based on the combination of adaptive dynamic programming (ADP) and sliding mode admittance control is proposed. The unknown environment is modeled as a linear model and based on quasi, a sliding mode admittance controller is derived to resist disturbance interference. An optimal control method that combines ADP with sliding mode admittance controller is proposed, in which the definition of R-matrix in value function is optimized and improved to further improve the tracking accuracy. The neural network based on ADP is …

Design And Simulation Of A Location Privacy Protection Scheme Based On Zero-Knowledge Proof For Military Iot, Mingjie Shi, Chengyu Xie, Chuanfu Zhang

Journal of System Simulation

Abstract: In the military Internet of Things (IoT) combat environment, the location privacy issue becomes a key challenge. An innovative location privacy protection scheme based on zero-knowledge proof is proposed to ensure that in unreliable communication channels, the location information of combat units can be verified without revealing their specific coordinates, so as to achieve the goal of protecting sensitive location information. Based on the idea of cryptography, by using zero-knowledge proof, through algebraic circuit, rank-1 constraint system(R1CS), quadratic arithmetic programs(QAP), and other steps, the position coordinate information proof problem is transformed into a point verification problem on a polynomial …

Time-Varying Rbf Neural Network-Based Controller Design For A Class Of Time-Varying Nonlinear Systems, Jing Li, Taotao Zhang, Kai Jin, Shengzhi Yuan, Zilong Zha

Journal of System Simulation

Abstract: A time-varying RBF neural network with time-varying properties is firstly proposed, and its approximation theorem is obtained. For a class of nonlinear systems with non-parametric time-varying uncertainties, the proposed time-varying RBF neural network is used to approximate the time-varying uncertainties, and the controller is designed by making use of Lyapunov stability theory and adaptive iterative learning control techniques. We obtain the stability theorem of the designed controller. The simulation results verify the effectiveness of the time-varying neural network and the correctness of the controller design scheme.

A Structured Conceptual Model Of Joint Operations From Design Perspective, Rui Wen

Journal of System Simulation

Abstract: With the development of technology, through the complementary interaction among operations, operation effectiveness can non-linearly increase and realize fissional and exponential effect. In order to realize dynamic convergence, it is necessary to carry out the action, information and energy unified design. From the antagonism view, taking into account the factors such as the purpose of the operation, the strength of the operation, the conditions of the operation, and so on, the joint operation is divided into the preorder action/ state, the major operational action, the counter-action, the response action, and the branch action, which is synthesized to major operation, …

Terrain Surface Texture Generation Networks For User Semantics Customization, Yan Gao, Jimeng Li, Jianzhong Xu, Hongyan Quan

Journal of System Simulation

Abstract: Customizing terrain based on user semantics has practical value in the virtual terrain modeling of military simulation applications. This study provides a terrain surface texture generation network (TSTG-Net) that can synthesize realistic terrain based on user input semantics. TSTG-Net is designed as a Pix2pix structure and is based on CGAN. It learns the topology of customized terrain by encoding and parsing user semantics and regards the semantics feature as the constraint of CGAN. In the generator-discriminator structure, user-customized semantics are used as the input, and the real terrain with semantics is employed as the ground truth in network optimization. …

Air Distribution Simulation And Comfort Evaluation Of Large Space Building Based On Rans And Les, Shen Zhang, Ming Cheng, Yifan Wang, Fankai Meng, Ting Li, Han Chen, Zhifeng Ji

Journal of System Simulation

Abstract: Air distribution simulation and thermal comfort evaluation for heating, ventilation and air conditioning (HVAC) design of large space buildings is of great significance for the human thermal comfort improvement and the energy consumption reduction. By combining the steady analysis based on RANS and the transient analysis of large eddy simulation (LES), an air distribution simulation and thermal comfort evaluation process in large space buildings is established. Due to the low calculation consumption, the steady analysis based on RANS is conducted to efficiently evaluate the thermal comfort and the air quality under multiple working conditions. Considering the high computational consumption …

A Fuzzy Group Decision-Making-Based Method For Green Supplier Selection And Order Allocation, Lu Liu, Wenxin Li, Xiao Song, Bingli Sun, Guanghong Gong

Journal of System Simulation

Abstract: With the intensity of market competitiveness, the worsening of the global environment, and the improvement of public concern about environmental protection, the issue of green purchasing has received considerable attention. The vast majority of existing studies on green purchasing have concentrated on supplier selection with green criteria, so as to realize sustainable operations, whereas it is more feasible and economical for businesses to obtain the proper products from adaptable and suitable suppliers at the right times, rates, and volumes, which is referred to as supplier selection and order allocation. To resolve the aforementioned two crucial challenges, we propose a …

An Automatic Code Generation Method For Generic Real-Time Hardware-In-The-Loop Simulation Based On Custom Wizard, Zihan Liu, Lingxiao Hou, Yang Li, Zhiguang Wang, Wulong Zhang

Journal of System Simulation

Abstract: For the capability improvement demands of automation and generalization hardware-in-theloop simulation system, an automatic code generation method for generic real-time hardware-in-the-loop simulation based on custom wizard is proposed. A modular and universal code template-based frame documents and professional resource library are constructed with years of technical accumulation in hardware-in-the-loop simulation. The responsive front-ends and scripts are designed by HTML, CSS and JavaScript and an universal automatic code generation software AutoSimRTX is developed, which effectively supports the construction of hardware-in-the-loop simulation system.

A Dynamic Online Dashboard For Tracking The Performance Of Division 1 Basketball Athletic Performance, Erica Juliano, Chelsea Thakkar, Christopher B. Taber, Mehul S. Raval, Kaya Tolga, Samah Senbel

School of Computer Science & Engineering Undergraduate Publications

Using Data Analytics is a vital part of sport performance enhancement. We collect data from the Division 1 'Women's basketball athletes and coaches at our university, for use in analysis and prediction. Several data sources are used daily and weekly: WHOOP straps, weekly surveys, polar straps, jump analysis, and training session information. In this paper, we present an online dashboard to visually present the data to the athletes and coaches. R shiny was used to develop the platform, with the data stored on the cloud for instant updates of the dashboard as the data becomes available. The performance of athletes …

Leveraging Vr/Ar/Mr/Xr Technologies To Improve Cybersecurity Education, Training, And Operations, Paul Wagner, Dalal Alharthi

Journal of Cybersecurity Education, Research and Practice

The United States faces persistent threats conducting malicious cyber campaigns that threaten critical infrastructure, companies and their intellectual property, and the privacy of its citizens. Additionally, there are millions of unfilled cybersecurity positions, and the cybersecurity skills gap continues to widen. Most companies believe that this problem has not improved and nearly 44% believe it has gotten worse over the past 10 years. Threat actors are continuing to evolve their tactics, techniques, and procedures for conducting attacks on public and private targets. Education institutions and companies must adopt emerging technologies to develop security professionals and to increase cybersecurity awareness holistically. …

Dynamic Mechanism Of Science Based Technological Innovation And Industrial Evolution—Take Semiconductor, Digital Computer And Radio Technologies As Examples, Yi Zhang, Qiang Yan

Bulletin of Chinese Academy of Sciences (Chinese Version)

By studying the technological innovation and industrial development process of semiconductor, digital computer and radio, this study analyzes the path, conditions and force of science-based technological innovation and its industrialization, establishes a chain reaction model of large-scale technological innovation and diffusion, and compares it with market-based technological innovation. It is found that the large-scale aggregation of scientific research institutions and industrial laboratories accelerates the speed of technological innovation, and diffuses along two paths of scientific research institutions to enterprises and enterprises to enterprises, forming a chain reaction of large-scale technological innovation. Strategic demand is the basic driving force for the …

Integrating Nist And Iso Cybersecurity Audit And Risk Assessment Frameworks Into Cameroonian Law, Bernard Ngalim

Journal of Cybersecurity Education, Research and Practice

This paper reviews cybersecurity laws and regulations in Cameroon, focusing on cybersecurity and information security audits and risk assessments. The importance of cybersecurity risk assessment and the implementation of security controls to cure deficiencies noted during risk assessments or audits is a critical step in developing cybersecurity resilience. Cameroon's cybersecurity legal framework provides for audits but does not explicitly enumerate controls. Consequently, integrating relevant controls from the NIST frameworks and ISO Standards can improve the cybersecurity posture in Cameroon while waiting for a comprehensive revision of the legal framework. NIST and ISO are internationally recognized as best practices in information …

Lrtransformer: Learn-Region Transformer For Object-Agnostic Point Cloud Segmentation, Dipesh Gyawali

LSU Master's Theses

3D point cloud segmentation segments the 3D point cloud data into different regions/instances depending on their features that have numerous applications in robotics, autonomous driving, digital twinning, augmented reality, etc. The majority of the existing point cloud segmentation methods depend on class labels to identify 3D objects in the surroundings. Our work focuses on segmenting point clouds into different regions/instances in an object-agnostic manner for any number of objects in the environment. Given the point cloud, our method can segment the entire scene into multiple instances without depending on object shape and size. We leverage the power of the self-attention …

Teacher Candidates’ Conceptions And Practices Of Computational Thinking For Equity, Heather F. Clark, Symone A. Gyles, Imelda Nava-Landeros

Journal of Computer Science Integration

This study documents novice science and math teachers’ developing pedagogical approaches to integrating computational thinking (CT) and data into their courses to support educational equity and social justice. The 10 novice teacher candidates (TCs) studied were part of an urban teacher residency program that empowered them with an asset-based pedagogy we describe as “CT for Equity.” Drawing on coursework and interviews as data, we asked three questions: What are teachers’ conceptions of CT? What are their CT instructional practices? And how did their students respond to those practices? To explore conceptions of CT, we used Kafai et al.’s (2020) articulation …

Smart Service Function Chain System For Dynamic Traffic Steering Using Reinforcement Learning (Chrl), Ahmed Nadhum, Ahmed Al-Saadi

Karbala International Journal of Modern Science

The rapid development of the Internet and network services coupled with the growth of communication infrastructure necessitates the employment of intelligent systems. The complexity of the network is heightened by these systems, as they offer diverse services contingent on traffic type, user needs, and security considerations. In this context, a service function chain offers a toolkit to facilitate the management of intricate network systems. However, various traffic types require dynamic adaptation in the sets of function chains. The problem of optimizing the order of service functions in the chain must be solved using the proposed approach, along with balancing the …

Enhancing Exploration-Exploitation In Harmony Search For Airborne Hyperspectral Imaging Band Selection (E3hs), Mohammed Abdulmajeed Moharram, Divya Meena Sundaram

Turkish Journal of Electrical Engineering and Computer Sciences

Hyperspectral imaging has emerged as a prominent area of research in the field of remote sensing science. However, hyperspectral images (HSIs) pose a notable challenge due to the presence of numerous irrelevant and redundant spectral bands exhibiting high correlation. Therefore, it is necessary to enhance the classification performance for HSI processing by selecting the most relevant discriminative spectral bands. To this end, this paper introduces a metaheuristic search method called enhancing exploration-exploitation in harmony search (E3HS). The standard harmony search suffers from many weaknesses, such as premature convergence and falling easily into the local optimum. Consequently, E3HS was proposed to …

Trcaptionnet: A Novel And Accurate Deep Turkish Image Captioning Model With Vision Transformer Based Image Encoders And Deep Linguistic Text Decoders, Serdar Yildiz, Abbas Memi̇ş, Songül Varli

Turkish Journal of Electrical Engineering and Computer Sciences

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, …

Cccd: Corner Detection And Curve Reconstruction For Improved 3d Surface Reconstruction From 2d Medical Images, Mriganka Sarmah, Arambam Neelima

Turkish Journal of Electrical Engineering and Computer Sciences

The conventional approach to creating 3D surfaces from 2D medical images is the marching cube algorithm, but it often results in rough surfaces. On the other hand, B-spline curves and nonuniform rational B-splines (NURBSs) offer a smoother alternative for 3D surface reconstruction. However, NURBSs use control points (CTPs) to define the object shape and corners play an important role in defining the boundary shape as well. Thus, in order to fill the research gap in applying corner detection (CD) methods to generate the most favorable CTPs, in this paper corner points are identified to predict organ shape. However, CTPs must …

Focal Modulation Network For Lung Segmentation In Chest X-Ray Images, Şaban Öztürk, Tolga Çukur

Turkish Journal of Electrical Engineering and Computer Sciences

Segmentation of lung regions is of key importance for the automatic analysis of Chest X-Ray (CXR) images, which have a vital role in the detection of various pulmonary diseases. Precise identification of lung regions is the basic prerequisite for disease diagnosis and treatment planning. However, achieving precise lung segmentation poses significant challenges due to factors such as variations in anatomical shape and size, the presence of strong edges at the rib cage and clavicle, and overlapping anatomical structures resulting from diverse diseases. Although commonly considered as the de-facto standard in medical image segmentation, the convolutional UNet architecture and its variants …

Infrared Imaging Segmentation Employing An Explainable Deep Neural Network, Xinfei Liao, Dan Wang, Zairan Li, Nilanjan Dey, Rs Simon, Fuqian Shi

Turkish Journal of Electrical Engineering and Computer Sciences

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice …

Feature Distillation From Vision-Language Model For Semisupervised Action Classification, Asli Çeli̇k, Ayhan Küçükmani̇sa, Oğuzhan Urhan

Turkish Journal of Electrical Engineering and Computer Sciences

The training of supervised machine learning approaches is critically dependent on annotating large-scale datasets. Semisupervised learning approaches aim to achieve compatible performance with supervised methods using relatively less annotation without sacrificing good generalization capacity. In line with this objective, ways of leveraging unlabeled data have been the subject of intense research. However, semisupervised video action recognition has received relatively less attention compared to image domain implementations. Existing semisupervised video action recognition methods trained from scratch rely heavily on augmentation techniques, complex architectures, and/or the use of other modalities while distillation-based methods use models that have only been trained for 2D …

Multi-View Brain Tumor Segmentation (Mvbts): An Ensemble Of Planar And Triplanar Attention Unets, Snehal Rajput, Rupal Kapdi, Mehul Raval, Mohendra Roy

Turkish Journal of Electrical Engineering and Computer Sciences

3D UNet has achieved high brain tumor segmentation performance but requires high computation, large memory, abundant training data, and has limited interpretability. As an alternative, the paper explores using 2D triplanar (2.5D) processing, which allows images to be examined individually along axial, sagittal, and coronal planes or together. The individual plane captures spatial relationships, and combined planes capture contextual (depth) information. The paper proposes and analyzes an ensemble of uniplanar and triplanar UNets combined with channel and spatial attention for brain tumor segmentation. It investigates the significance of each plane and analyzes the impact of uniplanar and triplanar ensembles with …

Hybrid Machine Learning Model To Predict Chronic Kidney Diseases Using Handcrafted Features For Early Health Rehabilitation, Amjad Rehman, Tanzila Saba, Haider Ali, Narmine Elhakim, Noor Ayesha

Turkish Journal of Electrical Engineering and Computer Sciences

Chronic kidney diseases proliferate due to hypertension, diabetes, anemia, obesity, smoking etc. Patients with such conditions are sometimes unaware of first symptoms, complicating disease diagnosis. This paper presents chronic kidney disease (CKD) prediction model to classify CKD patients from NCKD (Non-CKD). The proposed study has two main stages. First, we found the odds ratio through logistic regression and comparison test to identify early risk factors from kidneys? MRI and differentiate CKD from NCKD subjects. In stage 2, LR, LDA, MLP classifiers were applied to predict CKD and NCKD by extracting features from MRI. The odds ratio of blood glucose random …

A Unique Hybrid Domain Hand-Crafted Feature To Classify Colorectal Tissue Histopathological Images Using Multiheaded Cnn, Anurodh Kumar, Amit Vishwakarma, Varun Bajaj

Turkish Journal of Electrical Engineering and Computer Sciences

Early diagnosis of colorectal cancer lengthens human life and is helpful in efforts to cure the illness. Histopathological inspection is a routinely utilized technique to diagnose it. Visual assessment of histopathological images takes more investigation time, and the decision is based on the individual perceptions of clinicians. The existing methods for colorectal cancer classification use only spatial information. However, studies on the spectral domains of information are lacking in the literature. Therefore, the performance of the existing techniques is moderate. To improve the performance of colorectal cancer classification, this work proposes a unique hybrid domain hand-crafted feature formulated using scale-invariant …

Cognitive Digital Modelling For Hyperspectral Image Classification Using Transfer Learning Model, Mohammad Shabaz, Mukesh Soni

Turkish Journal of Electrical Engineering and Computer Sciences

Deep convolutional neural networks can fully use the intrinsic relationship between features and improve the separability of hyperspectral images, which has received extensive in recent years. However, the need for a large number of labelled samples to train deep network models limits the application of such methods. The idea of transfer learning is introduced into remote sensing image classification to reduce the need for the number of labelled samples. In particular, the situation in which each class in the target picture only has one labelled sample is investigated. In the target domain, the number of training samples is enlarged by …

Classification Of Chronic Pain Using Fmri Data: Unveiling Brain Activity Patterns For Diagnosis, Rejula V, Anitha J, Belfin Robinson

Turkish Journal of Electrical Engineering and Computer Sciences

Millions of people throughout the world suffer from the complicated and crippling condition of chronic pain. It can be brought on by several underlying disorders or injuries and is defined by chronic pain that lasts for a period exceeding three months. To better understand the brain processes behind pain and create prediction models for pain-related outcomes, machine learning is a potent technology that may be applied in Functional magnetic resonance imaging (fMRI) chronic pain research. Data (fMRI and T1-weighted images) from 76 participants has been included (30 chronic pain and 46 healthy controls). The raw data were preprocessed using fMRIprep …