Computer Engineering Commons^™

Feasibility And Outcomes Of Supplemental Gait Training By Robotic And Conventional Means In Acute Stroke Rehabilitation, Mukul Talaty, Alberto Esquenazi

Moss-Magee Rehabilitation Papers

INTRODUCTION: Practicality of implementation and dosing of supplemental gait training in an acute stroke inpatient rehabilitation setting are not well studied but can have positive impact on outcomes.

OBJECTIVES: To determine the feasibility of early, intense supplemental gait training in inpatient stroke rehabilitation, compare functional outcomes and the specific mode of delivery.

DESIGN AND SETTING: Assessor blinded, randomized controlled trial in a tertiary Inpatient Rehabilitation Facility.

PARTICIPANTS: Thirty acute post-stroke patients with unilateral hemiparesis (≥ 18 years of age with a lower limb MAS ≤ 3).

INTERVENTION: Lokomat® or conventional gait training (CGT) in addition to standard mandated therapy time. …

Nitrogen Radiofrequency Plasma Treatment Of Graphene, Antoine Bident, Nathalie Caillault, Florence Delange, Christine Labrugere, Guillaume Aubert, Cyril Aymonier, Etienne Durand, Alain Demourgues, Yongfeng Lu, Jean-François Silvain

Department of Electrical and Computer Engineering: Faculty Publications

The incorporation of nitrogen (N) atoms into a graphitic network such as graphene (Gr) remains a major challenge. However, even if the insertion mechanisms are not yet fully understood, it is certain that the modification of the electrical properties of Gr is possible according to the configuration adopted. Several simulations work, notably using DFT, have shown that the incorporation of N in Gr can induce an increase in the electrical conductivity and N acts as an electron donor; this increase is linked to the amount of N, the sp²/sp³ carbon configuration, and the nature of C-N bonding. …

Awareness Of Cybersecurity And Its Relationship To Digital Transformation Among Supervisors Of The Education Department In Jazan Region, Maryam Y. Otaif

International Journal for Research in Education

The study aimed to identify the degree of awareness of cybersecurity, and to reveal the reality of digital transformation and the relationship between them from the point of view of the supervisors of the Education Department in the Jazan region. The study followed the descriptive correlative approach, by stratified random sampling (257). a questionnaire was built , The study found that the degree of cybersecurity awareness is high with an arithmetic mean of (3.48), and also degree of awareness of cybersecurity concepts is high, (3,52), and awareness of cybersecurity risks and violations (3.45), and it was found that the total …

Enabling Intelligent Network Management Through Multi-Agent Systems: An Implementation Of Autonomous Network System, Petro Mushidi Tshakwanda

Electrical and Computer Engineering ETDs

This Ph.D. dissertation presents a pioneering Multi-Agent System (MAS) approach for intelligent network management, particularly suited for next-generation networks like 5G and 6G. The thesis is segmented into four critical parts. Firstly, it contrasts the benefits of agent-based design over traditional micro-service architectures. Secondly, it elaborates on the implementation of network service agents in Python Agent Development Environment (PADE), employing machine learning and deep learning algorithms for performance evaluation. Thirdly, a new scalable approach, Scalable and Efficient DevOps (SE-DO), is introduced to optimize agent performance in resource-constrained settings. Fourthly, the dissertation delves into Quality of Service (QoS) and Radio Resource …

Stability Of Deep Neural Networks For Feedback-Optimal Pinpoint Landings, Omkar S. Mulekar, Hancheol Cho, Riccardo Bevilacqua

Student Works

The ability to certify systems driven by neural networks is crucial for future rollouts of machine learning technologies in aerospace applications. In this study, the neural networks are used to represent a fuel-optimal feedback controller for two different 3-degree-of-freedom pinpoint landing problems. It is shown that the standard sum-ofsquares Lyapunov candidate is too restrictive to assess the stability of systems with fuel-optimal control profiles. Instead, a parametric Lyapunov candidate (i.e. a neural network) can be trained to sufficiently evaluate the closed-loop stability of fuel-optimal control profiles. Then, a stability-constrained imitation learning method is applied, which simultaneously trains a neural network …

Columnas: The Honors Program Newsletter At Bentley University, Hailey Jennato, Samson Shen, Clara Williams

Honors Program

Page 1: HOW AI IS IMPACTING THE BENTLEY CLASSROOM AND EDUCATION OVERALL ~ by Nayeli Franco ’24

Page 2: A BEAUTY OF DIVERSITY ~ by Yun Song ’26

Page 3: RESURRECTING THE DEAD THROUGH COMPUTER TECHNOLOGY: HONORING THEIR MEMORY OR EXPLOITING THEIR LEGACY? ~ by Hailey Jennato ’24

Page 4: THE IMPORTANCE OF DEVELOPING EMOTIONAL INTELLIGENCE ~ by Isa Ramirez Perdomo ’26

Page 5: FROM STRUGGLE TO STRENGTH: THRIVING AS AN INTERNATIONAL STUDENT ~ by Ledion Hoti ’25

Page 6: CHASING BUTTERFLIES ~ by Alyssa Galin ’27

The Role Of Feedback Within Scrum For Engineering Department Operations, Massood Towhidnejad, Omar Ochoa, James J. Pembridge, Radu Babiceanu

Posters

The Scrum framework is built on the principles of inspection and adaptation. Feedback drives the inspection process, and the team adapts based on that feedback to optimize its performance and outcomes. Within engineering departments, Scrum requires departments to examine how and when feedback is obtained to ensure that the department is remaining agile. This poster illustrates the role of feedback within two Scrum teams, one focused on student success and the other focused on faculty rewards and incentives. The two cases emphasize the need for continuous introspection at team and department levels.

Framework For Implementing Advanced Radar Plotting Aid Capability For Small Maritime Vessels, Jason Stark Harris

Electrical & Computer Engineering Theses & Dissertations

Every year in the United States many people are killed or injured when maritime vessels collide with other vessels or fixed objects. According to the United States Coast Guard, the top contributing factors to these collisions are operator inattention, operator inexperience and an improper lookout. Larger commercial vessels are required to have RADAR systems which support Automatic RADAR Plotting Aid (ARPA) which can automatically detect collisions and alert an operator to change course. These systems can be very expensive which put them out of reach of the average recreational boater. It is however possible to implement a low cost ARPA …

Drones For Marine Science And Agriculture, David Caldera, Sai Murthy

College of Engineering Summer Undergraduate Research Program

Our research project was launched at Cal Poly in 2019 with the goal of assisting researchers at the CSULB Shark Lab in detecting sharks from aerial images. Under the guidance of Dr. Franz J. Kurfess, students trained an object detection algorithm using shark images and were able to achieve high rate of detection. Following this success, the team has constructed multiple drones and expanded their research to include applications in the fields of agriculture and ecology. This summer the goal is to use a iPhone 14 Pro in lieu of a traditional camera system for real-time object recognition. Object detection …

Building A Benchmark For Industrial Iot Application, Pranay K. Tiru, Soma Tummala

College of Engineering Summer Undergraduate Research Program

In this project, we have developed a rather robust means of processing and displaying large sums of IoT data using several cutting-edge, industry-standard technologies. Our data pipeline integrates physical sensors that send various environmental data like temperature, humidity, and pressure. Once created, the data is then collected at an MQTT broker, streamed through a Kafka cluster, processed within a Spark Cluster, and stored in a Cassandra database.

In order to test the rigidity of the pipeline, we also created virtual sensors. This allowed us to send an immense amount of data, which wasn’t necessarily feasible with just the physical sensors. …

Exploring Cognition And Affect During Human-Cobot Interaction, Angelika T. Canete, Javier Gonzalez-Sanchez, Rafael Guerra Silva

College of Engineering Summer Undergraduate Research Program

Collaborative robots (Cobots) have recently gained popularity due to their capability to work collaboratively with human operators. This collaborative relationship has been named under the robotics discipline of Human-Robot Collaboration (HRC), in which humans and robots work together to accomplish a common task while also being in the same physical space. An important part of collaboration is the human's decision-making, which is largely affected by their affective and cognitive state. A cobot lacks this fundamental understanding of the human operator. In this research, we utilize a server-client program to communicate the affective states of a human user to a Raspberry …

Modeling And Compensating Of Noise In Time-Of-Flight Sensors, Bryan Rodriguez

Electrical Engineering Theses and Dissertations

Three-dimensional (3D) sensors provide the ability to perform contactless measurements of objects and distances that are within their field of view. Unlike traditional two-dimensional (2D) cameras, which only provide RGB data about objects within a scene, 3D sensors are able to directly provide depth information for objects within a scene. Of these 3D sensing technologies, Time-of-Flight (ToF) sensors are becoming more compact which allows them to be more easily integrated with other devices and to find use in more applications. ToF sensors also provide several benefits over other 3D sensing technologies that increase the types of applications where ToF sensors …

Deep-Learning-Based Classification Of Digitally Modulated Signals, John A. Snoap

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents several novel deep-learning (DL)-based approaches for classifying digitally modulated signals, one method of which involves the use of capsule networks (CAPs) together with cyclic cumulant (CC) features of the signals. These were blindly estimated using cyclostationary signal processing (CSP) and were then input into the CAP for training and classification. The classification performance and the generalization abilities of the proposed approach were tested using two distinct datasets that contained the same types of digitally modulated signals but had distinct generation parameters. The results showed that the classification of digitally modulated signals using CAPs and CCs proposed in …

2023 (Fall) Ensi Informer Magazine, Morehead State University. Engineering Sciences Department

ENSI Informer Magazine Archive

The ENSI Informer Magazine published in the fall of 2023.

Towards Agile Academia: An Approach To Scientific Paper Writing Inspired By Software Engineering, Tyler Procko

Doctoral Dissertations and Master's Theses

The construction of scientific papers is performed in service of the greater scientific community. This iterative process is, in effect, an academic economy, where all members benefit from well-written papers. However, many published scientific papers are poorly written; they often lack sufficient detail to allow replication, there is improper usage of citations or a lack of regard to relevant work, reporting is vague or without linked empirical data to allow verification, figures do not correspond to text or are non-sensical, literary elements, e.g., bulleted lists, are used ineffectively, formatting renders certain sections unreadable, and grammatical errors abound. The issues of …

Balanced Blended Space: Proposing A Universal Theoretical Framework For Combinative Reality, David Smith, Frederick Bianchi

Publications and Research

In today's fragmented societies, a unified framework for communication and collaboration across different realities is crucial. We introduce Balanced Blended Space (BBS) as a framework for describing combinative reality, encompassing virtual, physical, and conceptual realms, all intrinsically connected. Interactions within these environments shape our perceptual space. This paper outlines key axiomatic assumptions, criteria for a universal framework, and fundamental terminology. We identify deep symmetries enabling the BBS framework, including Cognitive and Computational Symmetry, Physical and Virtual Symmetry, Mediation Pathway Symmetry, Space-Time Symmetry, and Sensory Symmetry. We propose tests to determine its viability, emphasizing virtual intelligence as a collaborative partner. We …

‘Unbelievable’: Perspectives On The Digital World In Manang, Alex Halaby

Independent Study Project (ISP) Collection

Manang lies one hundred miles northwest of Kathmandu in a valley called Nyeshang. Until a major road was constructed in 2014, the challenge of importing goods in and out of the town slowed the development of digital infrastructures supporting internet use. Consequently, the widespread use of the internet and digital communication is a relatively new phenomenon. Already, its effects are visible in classrooms, health clinics, hotels, and social structures. This paper investigates how Manangis learn about themselves, each other, and the world outside the valley in this new digital space. Emphasis is not placed on quantifications, but rather on the …

Designing Tomorrow's Reality: The Development And Validation Of An Augmented And Mixed Reality Heuristic Checklist, Jessyca Derby

Doctoral Dissertations and Master's Theses

Augmented (AR) and Mixed Reality (MR) are new and currently developing technologies. They have been used and shown promise and popularity in the domains of education, training, enterprise, retail, consumer products, and more. However, there is a lack of consistency and standards in AR and MR devices and applications. Interactions and standards in one application may drastically differ from another. This may make it difficult for users, especially those new to these technologies, to learn and feel comfortable using the devices or applications. It may also hinder the usability of the applications as designers may not follow proven techniques to …

Objectfusion: Multi-Modal 3d Object Detection With Object-Centric Fusion, Q. Cai, Y. Pan, T. Yao, Chong-Wah Ngo, T. Mei

Research Collection School Of Computing and Information Systems

Recent progress on multi-modal 3D object detection has featured BEV (Bird-Eye-View) based fusion, which effectively unifies both LiDAR point clouds and camera images in a shared BEV space. Nevertheless, it is not trivial to perform camera-to-BEV transformation due to the inherently ambiguous depth estimation of each pixel, resulting in spatial misalignment between these two multi-modal features. Moreover, such transformation also inevitably leads to projection distortion of camera image features in BEV space. In this paper, we propose a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for …

Prirpt: Practical Blockchain-Based Privacy-Preserving Reporting System With Rewards, Rui. Shi, Yang Yang, Huamin. Feng, Feng. Yuan, Huiqin. Xie, Jianyi. Zhang

Research Collection School Of Computing and Information Systems

In order to obtain evidence of a crime timely, most authorities encourage whistleblowers to provide valuable reports by rewarding them with prizes. However, criminals will try their best to delete or tamper with the reports and even threaten and revenge the whistleblowers to escape punishment. Hence, to make the reporting system work, it is essential to ensure the integrity of reported messages and the anonymity of the reporting and rewarding procedures in the reporting system. Most existing schemes for this problem are generally based on ring signatures, which incur high computational overhead and imperfect anonymity. In this paper, we introduce …

Owner-Free Distributed Symmetric Searchable Encryption Supporting Conjunctive Queries, Qiuyun Tong, Xinghua Li, Yinbin Miao, Yunwei Wang, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an Owner-free Distributed Symmetric searchable encryption supporting Conjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) …

Adaptive Octree Meshes For Simulation Of Extracellular Electrophysiology, Christopher Bc Girard, Dong Song

Engineering Faculty Articles and Research

Objective. The interaction between neural tissues and artificial electrodes is crucial for understanding and advancing neuroscientific research and therapeutic applications. However, accurately modeling this space around the neurons rapidly increases the computational complexity of neural simulations. Approach. This study demonstrates a dynamically adaptive simulation method that greatly accelerates computation by adjusting spatial resolution of the simulation as needed. Use of an octree structure for the mesh, in combination with the admittance method for discretizing conductivity, provides both accurate approximation and ease of modification on-the-fly. Main results. In tests of both local field potential estimation and multi-electrode stimulation, dynamically adapted meshes …

Stepwise Dynamic Nearest Neighbor (Sdnn): A New Algorithm For Classification, Deni̇z Karabaş, Derya Bi̇rant, Peli̇n Yildirim Taşer

Turkish Journal of Electrical Engineering and Computer Sciences

Although the standard k-nearest neighbor (KNN) algorithm has been used widely for classification in many different fields, it suffers from various limitations that abate its classification ability, such as being influenced by the distribution of instances, ignoring distances between the test instance and its neighbors during classification, and building a single/weak learner. This paper proposes a novel algorithm, called stepwise dynamic nearest neighbor (SDNN), which can effectively handle these problems. Instead of using a fixed parameter k like KNN, it uses a dynamic neighborhood strategy according to the data distribution and implements a new voting mechanism, called stepwise voting. Experimental …

Cognitive Load Detection Using Ci-Ssa For Eeg Signal Decomposition And Nature-Inspired Feature Selection, Jammisetty Yedukondalu, Lakhan Dev Sharma

Turkish Journal of Electrical Engineering and Computer Sciences

Cognitive load detection is eminent during the mental assignment of neural activity because it indicates how the brain reacts to stimuli. The level of cognitive load experienced during mental arithmetic tasks can be determined using an electroencephalogram (EEG). The EEG data were collected from publicly available datasets, namely, mental arithmetic task (MAT) and simultaneous task workload (STEW). The first phase comprises decomposing the electroencephalogram (EEG) signal into intrinsic mode functions (IMFs) using circulant singular spectrum analysis (Ci-SSA). In the second phase, entropy-based features were evaluated using IMFs. After that, the extracted features were fed to nature-inspired feature selection algorithms: genetic …

Using T-Distributed Stochastic Neighbor Embedding For Visualization And Segmentation Of 3d Point Clouds Of Plants, Heli̇n Dutağaci

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, the use of t-SNE is proposed to embed 3D point clouds of plants into 2D space for plant characterization. It is demonstrated that t-SNE operates as a practical tool to flatten and visualize a complete 3D plant model in 2D space. The perplexity parameter of t-SNE allows 2D rendering of plant structures at various organizational levels. Aside from the promise of serving as a visualization tool for plant scientists, t-SNE also provides a gateway for processing 3D point clouds of plants using their embedded counterparts in 2D. In this paper, simple methods were proposed to perform semantic …

Direct Pore-Based Identification For Fingerprint Matching Process, Vedat Delican, Behçet Uğur Töreyi̇n, Ege Çeti̇n, Ayli̇n Yalçin Saribey

Turkish Journal of Electrical Engineering and Computer Sciences

Fingerprints are one of the most important scientific proof instruments in solving forensic cases. Identification in fingerprints consists of three levels based on the flow direction of the papillary lines at the first level, the minutiae points at the second level, and the pores at the third level. The inadequacy of existing imaging systems in detecting fingerprints and the lack of pore details at the desired level limit the widespread use of third-level identification. The fact that fingerprints with images based on pores in the unsolved database are not subjected to any evaluation criteria and remain in the database reveals …

Dynamic Deep Neural Network Inference Via Adaptive Channel Skipping, Meixia Zou, Xiuwen Li, Jinzheng Fang, Hong Wen, Weiwei Fang

Turkish Journal of Electrical Engineering and Computer Sciences

Deep neural networks have recently made remarkable achievements in computer vision applications. However, the high computational requirements needed to achieve accurate inference results can be a significant barrier to deploying DNNs on resource-constrained computing devices, such as those found in the Internet-of-things. In this work, we propose a fresh approach called adaptive channel skipping (ACS) that prioritizes the identification of the most suitable channels for skipping and implements an efficient skipping mechanism during inference. We begin with the development of a new gating network model, ACS-GN, which employs fine-grained channel-wise skipping to enable input-dependent inference and achieve a desirable balance …

Joint Intent Detection And Slot Filling For Turkish Natural Language Understanding, Osman Büyük

Turkish Journal of Electrical Engineering and Computer Sciences

Intent detection and slot filling are two crucial subtasks of a text-based goal-oriented dialogue system. In a goal-oriented dialogue system, users interact with the system to complete a goal (or to fulfill their intent) and provide the necessary information (slot values) to achieve that goal. Therefore, a user?s text input includes information about the user?s intent and contains required slot values. Recently, joint models that simultaneously detect the intent and extract the slots are proposed to benefit from the interaction between the two tasks. The proposed methods are usually tested using benchmark data sets in English such as ATIS and …

Recognizing Handwritten Digits Using Spiking Neural Networks With Learning Algorithms Based On Sliding Mode Control Theory, Yeşi̇m Öni̇z, Mehmet Ayyildiz

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a spiking neural network (SNN) has been proposed for recognizing the digits written on the LCD screen of an experimental setup. The convergence of the learning algorithm has been ensured by using sliding mode control (SMC) theory and the Lyapunov stability method for the adaptation of the network parameters. The spike response model (SRM) has been utilized in the design of the SNN. The performance of the proposed learning scheme has been evaluated both on the experimental data and on the MNIST dataset. The simulated and experimental results of the SNN structure have been compared with the …

Transforming Temporal-Dynamic Graphs Into Time-Series Data For Solving Event Detection Problems, Kutay Taşci, Fuat Akal

Turkish Journal of Electrical Engineering and Computer Sciences

Event detection on temporal-dynamic graphs aims at detecting significant events based on deviations from the normal behavior of the graphs. With the widespread use of social media, many real-world events manifest as social media interactions, making them suitable for modeling as temporal-dynamic graphs. This paper presents a workflow for event detection on temporal-dynamic graphs using graph representation learning. Our workflow leverages generated embeddings of a temporal-dynamic graph to reframe the problem as an unsupervised time-series anomaly detection task. We evaluated our workflow on four distinct real-world social media datasets and compared our results with the related work. The results show …