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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Chimehi, Bahareh Wallace, Bruce Carleton University SAM3 Innovation Hub Department of Systems and Computer Engineering Ottawa Canada Carleton University Bruyère Research Institute SAM3 Innovation Hub Department of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665493840

This work demonstrates the use of Decision Tree and Random Forest machine learning to determine the direction of desired movement from the forces applied to two push-handles on a novel patient transfer system the Able Innovations ALTA™ patient transfer system provides a replacement transfer method for residents in care that cannot transfer on their own that have limited mobility. This new system is significantly heavier that a hospital gurney because of the weight of the mechatronics and healthcare professionals will need power assist to transport and position the system. In this work, two loadcell sensor-based prototypes have been used to measure the input forces and direction applied by a user to two handles. These two prototypes have been placed on a hospital bed to simulate the two handles on the right and left provided to hospital staff to push the system. Machine learning is used to analyze sensor measurements from each handle to predict the movement intent. The results are presented for test pushes in 6 directions that include forward and reverse in each of straight, left, and right turns. Two models of Machine Learning (decision tree classifier and random forest classifier) have been used for 8 and 14 features and are shown to be able to predict the direction of push with high accuracy. The accuracy for 8 features using decision tree and random forest has been measured 93.5% and 97.0% respectively. © 2023 IEEE.

关键词： Decision trees

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Privacy-Preserving Intelligent Intent-Based Network Slicing for IoT systems

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IEEE Internet of Things Journal 2025年

作者： Hussein, Dana Haj Ibnkahla, Mohamed Carleton University Department of Systems and Computer Engineering OttawaON Canada

The proliferation of Internet of Things (IoT) services across diverse sectors such as healthcare, industrial IoT, and smart cities has introduced unprecedented complexity in network Management and Orchestration (MO). Contemporary MO systems are challenged to support the coexistence of IoT services with varying Quality of Service (QoS) requirements while ensuring end-to-end (E2E) performance across heterogeneous technologies and multi-administrator domain networks. To address these requirements, emerging technologies such as Intelligent Intent-Based Network Slicing (I-IBNS) systems are increasingly employed, leveraging advancements in network automation, Artificial Intelligence (AI), and Network Slicing (NS). This paper focuses on three critical challenges in developing I-IBNS systems for IoT: E2E resource allocation, privacy preservation in multi-administrator systems, and E2E QoS assurance across heterogeneous networks and time-varying traffic. We propose a privacy-preserving I-IBNS framework, named Harmony Slice Master (H-SliceMaster), which integrates a knowledge management framework for privacy-aware data aggregation, an intent propagation mechanism for translating high-level intents into network configurations, and a novel Promise and Price Network Operation (PPNO) principle for optimizing E2E resource allocation while maintaining intra-domain privacy. A proof-of-concept design of the H-SliceMaster is presented, utilizing Deep Q-Networks (DQN) for intra-domain resource allocation and a centralized algorithm for optimizing E2E network slice deployment. Simulation results demonstrate that the H-SliceMaster efficiently satisfies various IoT applications and delay requirements. Moreover, the proposed system achieved an 20% and 50% reduction in system costs compared to a Branching Dueling Q-Network (BDQ) and greedy algorithm, respectively. © 2014 IEEE.

关键词： Resource allocation

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Performance Measurement of IoT Traffic Through SRv6 Network Programming

Performance Measurement of IoT Traffic Through SRv6 Network ...

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2022 IEEE International Conference on Communications Workshops, ICC Workshops 2022

作者： Kuri, Sajib Kumar Eldosouky, Abdelrahman Ibnkahla, Mohamed Carleton University Department of Systems and Computer Engineering Ottawa Canada

ISBN: (数字)9781665426718

ISBN: (纸本)9781665426718

Segment Routing over IPv6 (SRv6) is a modern networking technology for source routing that is envisioned to achieve high reliability, availability, and scalability of current IoT networks. Based on the IoT use-case scenario, traffic requires processing at both the edge and the cloud, and, thus it must be forwarded through the service provider's core networks. Using SRv6, IoT traffic can be forwarded over IPv6 without additional protocol overhead. However, performance measures are still needed to evaluate SRv6 behaviors or functions. To this end, the goal of this paper is to develop a realistic IoT traffic profile and use it to measure the performance of SRv6 behaviors. In particular, an SRv6 programming model is proposed that consists of three modules: Orchestrator, System Under Test (SUT), and Traffic Generator (TG). The proposed model considers IoT traffic forwarding through core and ensures reliability and quality of service (QoS). Then, different benchmarking methodologies, i.e., no-drop rate (NDR), partial drop rate (PDR), and maximum receive rate (MRR) are implemented to measure the performance of SRv6 policy headend and endpoint behaviors. Finally, a novel finder algorithm for MRR is proposed to automate the MRR benchmarking in the Linux kernel deployment. Implementation results are used to provide insights on the use of SRv6 behaviors to forward different IoT applications traffic based on the functional service requirements. © 2022 IEEE.

关键词： Benchmarking

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Sources of error during inertial sensing of human movement: a critical review of the fundamentals

Sources of error during inertial sensing of human movement: ...

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2023 IEEE International Symposium on Medical Measurements and Applications, MeMeA 2023

作者： Beange, Kristen H.E. Chan, Adrian D.C. Graham, Ryan B. Carleton University Department of Systems and Computer Engineering Ottawa Canada University of Ottawa School of Human Kinetics Ottawa Canada

ISBN: (纸本)9781665493840

Inertial assessments of human movement have potential to support diagnosis and treatment of neuromuscular disorders in healthcare settings. Despite the potential advantages, uptake and acceptance by healthcare professionals are still a challenge, as inertial measurement units are prone to measurement errors due to inherent limitations with the technology. As such, full exploitation is limited to a small group of highly qualified personnel. For usage to be more ubiquitous, standard practices for acquiring high-quality data are required and should include methods for error avoidance, detection, identification, quantification, and mitigation. In this paper, a critical review of sources of error was conducted, from which a taxonomic error classification framework was developed. From this review, it has become apparent which sources of error carry the highest risk for impacting data quality. Methods for error mitigation have been identified, along with limitations and areas for improvement. This framework is intended to serve as a useful reference for both proficient and non-proficient users to ensure all sources of error are considered when developing and interpreting IMU-based assessments. It also provides a foundation for developing standard practices to help users efficiently and reliably acquire high-quality data, which is imperative for uptake and acceptance in healthcare settings. © 2023 IEEE.

关键词： Measurement errors

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Object Detection Using Efficient Partitioning and Frame Reduction

Object Detection Using Efficient Partitioning and Frame Redu...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Omar Imran Sreeraman Rajan Shikharesh Majumdar Systems and Computer Engineering Carleton University Ottawa Canada

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Rapid object detection is crucial for safety-critical applications, such as post-event analysis of surveillance videos in crime investigations and the operation of autonomous vehicles. The objective of this paper is to improve the speed of object detection through efficient partitioning and frame reduction using parallel processing. Techniques to improve Spark's default partitioning by using entropy-based algorithms to create more evenly distributed partitions based on the estimated workload of the frames are considered. Redundant frames are removed to efficiently reduce the workload. Algorithms for removing redundant frames are introduced and evaluated for their effectiveness in comparison to random and fixed frame removals. The results from this paper demonstrate that partitioning algorithms that estimate workload using entropy provide faster processing time when compared to those that use random partitioning. The results also indicate that frame removal algorithms that use frame-specific information, like entropy difference between frames, further improve performance without notably reducing detection accuracy when compared to those that do not utilize frame-specific information.

关键词： Accuracy System performance Surveillance Object detection Parallel processing Transformers Entropy Partitioning algorithms Sparks Videos

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An Accurate and Low-Parameter Machine Learning Architecture for Next Location Prediction

arXiv

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arXiv 2024年

作者： Jary, Calvin Kahani, Nafiseh Systems and Computer Engineering Carleton University Canada

Next location prediction is a discipline that involves predicting a user’s next location. Its applications include resource allocation, quality of service, energy efficiency, and traffic management. This paper proposes an energy-efficient, small, and low parameter machine learning (ML) architecture for accurate next location prediction, deployable on modest base stations and edge devices. To accomplish this we ran a hundred hyperparameter experiments on the full human mobility patterns of an entire city, to determine an exact ML architecture that reached a plateau of accuracy with the least amount of model parameters. We successfully achieved a reduction in the number of model parameters within published ML architectures from 202 million down to 2 million. This reduced the total size of the model parameters from 791 MB down to 8 MB. Additionally, this decreased the training time by a factor of four, the amount of graphics processing unit (GPU) memory needed for training by a factor of twenty, and the overall accuracy was increased from 80.16% to 82.54%. This improvement allows for modest base stations and edge devices which do not have a large amount of memory or storage, to deploy and utilize the proposed ML architecture for next location prediction. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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A Semisupervised Approach to Predicting a Twitch Streamer's Growth based on the Game Streamed

A Semisupervised Approach to Predicting a Twitch Streamer's ...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Dybka, Andrew Kocjan, Dominic Ajila, Samuel A. Carleton University Dept. of Systems and Computer Engineering Ottawa Canada

ISBN: (纸本)9781665480451

Games live streaming is growing rapidly as a form of entertainment. A game streamer will like to know what game to stream in order to attract huge number of viewers and followers which in turn will generate sizable income for the streamer. Using streamer's metrics, the main goal of this research work is to design and develop a set of resources that a streamer can use to maximize the number of viewers and followers for a particular game and when to play the game. This research develops two models using machine learning techniques that can be used by game streamers to maximum the returns on investment. When both model predictions are presented as percentage, Model 1 using regression algorithms provides a MAE of 5.48 meaning the prediction has an error within 5.48% of the streamer's total follower count. Also, Model 1 has 85.46% of its predictions' absolute error less than or equal to 5. Similarly Model 2 with 2.53 MAE and 87.68% of its predictions' absolute error less than or equal to 5. © 2022 IEEE.

关键词： Forecasting

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Using CNN-LSTM Model for Weather Forecasting

Using CNN-LSTM Model for Weather Forecasting

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Fan, Michael Imran, Omar Singh, Arka Ajila, Samuel A. Carleton University Ottawa Department of Systems and Computer Engineering Ontario Canada

ISBN: (纸本)9781665480451

An efficient and cost-effective weather forecasting approach can be used to protect humans and benefit economic growth as a result of secure forest, agriculture, and tourism industry sectors. This paper is based on the IEEE Big Data IARAI's Weather4cast 2021 challenge dataset. The goal of this paper is to consider computational cost of predicting future weather forecast by using a CNN-LSTM based neural network model. The network utilizes an encoder-decoder architecture to predict future weather images. All the four variables are predicted using the same model providing generalization in the solution. The model is trained and tested on the Nile Region (R1) data and a significant improvement is observed for the loss against cloud mask and rainfall feature prediction in comparison with CNNGRU deep learning model. Two models - shallow and deep models are compared and the results in terms of MSE values for the shallow model (which is computationally cost effective) is not too far from the deep model. © 2022 IEEE.

关键词： Neural network models

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MPC Based Linear Equivalence with Control Barrier Functions for VTOL-UAVs

MPC Based Linear Equivalence with Control Barrier Functions ...

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2024 American Control Conference, ACC 2024

作者： Ali, Ali Mohamed Hashim, Hashim A. Shen, Chao Carleton University Department of Mechanical and Aerospace Engineering OttawaONK1S-5B6 Canada Carleton University Department of Systems and Computer Engineering OttawaONK1S-SB6 Canada

ISBN: (纸本)9798350382655

In this work, we propose a cascaded scheme of linear Model prediction Control (MPC) based on Control Barrier Functions (CBF) with Dynamic Feedback Linearization (DFL) for Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). CBF is a tool that allows enforcement of forward invariance of a set using Lyapunov-like functions to ensure safety. The First control synthesis that employed CBF was based on Quadratic Program (QP) that modifies the existing controller to satisfy the safety requirements. However, the CBF-QP-based controllers leading to longer detours and undesirable transient performance. Recent contributions utilize the framework of MPC benefiting from the prediction capabilities and constraints imposed on the state and control inputs. Due to the intrinsic nonlinearities of the dynamics of robotics systems, all the existing MPC-CBF solutions rely on nonlinear MPC formulations or operate on less accurate linear models. In contrast, our novel solution unlocks the benefits of linear MPC-CBF while considering the full underactuated dynamics without any linear approximations. The cascaded scheme converts the problem of safe VTOL-UAV navigation to a Quadratic Constraint Quadratic Programming (QCQP) problem solved efficiently by off-the-shelf solvers. The closed-loop stability and recursive feasibility is proved along with numerical simulations showing the effective and robust solutions. © 2024 AACC.

关键词： Quadratic programming

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