Mid Sweden University

miun.sePublications
Change search
Link to record
Permanent link

Direct link
Publications (10 of 70) Show all publications
Phan, T., Xu, Y., Kanoun, O., Oelmann, B. & Bader, S. (2024). Automated Ortho- Planar Spring Design for Vibration Energy Harvesters. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Open this publication in new window or tab >>Automated Ortho- Planar Spring Design for Vibration Energy Harvesters
Show others...
2024 (English)In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

Energy harvesting has been proposed to support or replace battery-based energy supplies in low-power loT systems. However, manual design and optimization is currently required to implement energy harvesters. This implies significant time and cost requirements, reducing the competitiveness of energy harvesting solutions in many applications. In this paper, we propose and study a method for the automated design of ortho-planar springs in vibration energy harvesters. The proposed method uses a Python tool that has been developed to translate parametric descriptions of the spring into 2D and 3D spring CAD models. The tool is combined with a black-box optimization algorithm in order to identify optimized spring parameters. An evaluation of the proposed method on a case study of an electromagnetic vibration energy harvester demonstrates that the approach successfully results in spring designs that perform well in the energy harvesting application. Consequently, the requirement of expert competence to adjust an energy harvester to new application constraints is significantly reduced, contributing to the availability and competitiveness of energy harvesting solutions in real world applications. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
design automation, low-power sensing systems, ortho-planar springs, vibration energy harvesting
National Category
Computational Mathematics
Identifiers
urn:nbn:se:miun:diva-52590 (URN)10.1109/SAS60918.2024.10636429 (DOI)001304520300028 ()2-s2.0-85203713371 (Scopus ID)9798350369250 (ISBN)
Conference
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Available from: 2024-09-24 Created: 2024-09-24 Last updated: 2024-11-25Bibliographically approved
Muthumala, U., Zhang, Y., Martinez Rau, L. & Bader, S. (2024). Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis. In: 2024 IEEE 10th World Forum on Internet of Things (WF-IoT): . Paper presented at 10th IEEE World Forum on Internet of Things, WF-IoT 2024, Ottawa, Canada, 10 November - 13 November, 2024. IEEE conference proceedings
Open this publication in new window or tab >>Comparison of Tiny Machine Learning Techniques for Embedded Acoustic Emission Analysis
2024 (English)In: 2024 IEEE 10th World Forum on Internet of Things (WF-IoT), IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

This paper compares machine learning approaches with different input data formats for the classification of acoustic emission (AE) signals. AE signals are a promising monitoring technique in many structural health monitoring applications. Machine learning has been demonstrated as an effective data analysis method, classifying different AE signals according to the damage mechanism they represent. These classifications can be performed based on the entire AE waveform or specific features that have been extracted from it. However, it is currently unknown which of these approaches is preferred. With the goal of model deployment on resource-constrained embedded Internet of Things (IoT) systems, this work evaluates and compares both approaches in terms of classification accuracy, memory requirement, processing time, and energy consumption. To accomplish this, features are extracted and carefully selected, neural network models are designed and optimized for each input data scenario, and the models are deployed on a low-power IoT node. The comparative analysis reveals that all models can achieve high classification accuracies of over 99\%, but that embedded feature extraction is computationally expensive. Consequently, models utilizing the raw AE signal as input have the fastest processing speed and thus the lowest energy consumption, which comes at the cost of a larger memory requirement.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
TinyML, acoustic emission, machine learning, structural health monitoring, feature extraction
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-51320 (URN)10.1109/WF-IoT62078.2024.10811219 (DOI)979-8-3503-7301-1 (ISBN)
Conference
10th IEEE World Forum on Internet of Things, WF-IoT 2024, Ottawa, Canada, 10 November - 13 November, 2024
Available from: 2025-02-11 Created: 2024-05-13 Last updated: 2025-02-11Bibliographically approved
Nguyen Phan, T., Xu, Y., Oelmann, B. & Bader, S. (2024). Electromagnetic Vibration Energy Harvester with Replaceable Ortho-planar Springs. In: 2024 IEEE SENSORS: . Paper presented at Proceedings of IEEE Sensors. IEEE conference proceedings
Open this publication in new window or tab >>Electromagnetic Vibration Energy Harvester with Replaceable Ortho-planar Springs
2024 (English)In: 2024 IEEE SENSORS, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes and investigates a novel electro-magnetic vibration energy harvester using ortho-planar springs. Addressing the limited output power of previously reported designs, the proposed harvester targets milliwatt level power outputs to be relevant for practical sensing and IoT applications. The proposed design employs an optimized pickup unit with Halbach array configuration and an optimized ortho-planar spring, which are integrated in a 3D-printed housing. During experimental evaluations, the harvester demonstrates an output power of up to 5.26mW and a normalized power density of 133.05 μW/(cm3∗g2), exceeding the performance of previously reported harvesters with ortho-planar springs. Moreover, exploiting the versatility of ortho-planar spring designs, it is shown that the harvester's frequency response can easily be adjusted to different application conditions. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
electromagnetic transduction, energy harvesting, ortho-planar springs, vibration energy harvester
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-53682 (URN)10.1109/SENSORS60989.2024.10785000 (DOI)2-s2.0-85215272025 (Scopus ID)9798350363517 (ISBN)
Conference
Proceedings of IEEE Sensors
Available from: 2025-01-28 Created: 2025-01-28 Last updated: 2025-01-28Bibliographically approved
Zhang, Y., Martinez Rau, L., Oelmann, B. & Bader, S. (2024). Enabling Autonomous Structural Inspections with Tiny Machine Learning on UAVs. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Open this publication in new window or tab >>Enabling Autonomous Structural Inspections with Tiny Machine Learning on UAVs
2024 (English)In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

Visual structural inspections in Structural Health Monitoring (SHM) are an important method to ensure the safety and long lifetime of infrastructures. Unmanned Aerial Vehicles (UAVs) with Deep Learning (DL) have gained in popularity to automate these inspections. Yet, the vast majority of research focuses on algorithmic innovations that neglect the availability of reliable generalized DL models, as well as the effect that the model's energy consumption would have on the UAV flight time. This paper highlights the performance of 14 popular CNN models with less than six million parameters for crack detection in concrete structures. Seven of these models were successfully deployed to a low-power, resource-constrained mi-crocontroller using Tiny Machine Learning (TinyML). Among the deployed models, MobileNetV1-x0.25 achieves the highest test accuracy (75.83%) and F1-Score (0.76), the second-lowest flash memory usage (273.5 kB), the second-lowest RAM usage (317.1kB), the fourth-fastest single-trial inference time (15.8ms), and the fourth-lowest number of Multiply-Accumulate operations (MACC) (42126514). Lastly, a hypothetical study of the DJI Mini 4 Pro UAV demonstrated that the TinyML model's energy consumption has a negligible impact on the UAV flight time (34 minutes vs. 33.98 minutes). Consequently, this feasibility study paves the way for future developments towards more efficient, autonomous unmanned structural health inspections. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
convolutional neural networks, damage classification, embedded systems, structure health monitoring, Tiny machine learning, unmanned aerial vehicles
National Category
Computer Sciences
Identifiers
urn:nbn:se:miun:diva-52591 (URN)10.1109/SAS60918.2024.10636583 (DOI)001304520300085 ()2-s2.0-85203704393 (Scopus ID)9798350369250 (ISBN)
Conference
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Available from: 2024-09-24 Created: 2024-09-24 Last updated: 2024-11-25Bibliographically approved
Jansen, K., Shallari, I., Mourad, S., Werheit, P. & Bader, S. (2024). Image-Based Condition Monitoring of Air-Spinning Machines with Deep Neural Networks. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Open this publication in new window or tab >>Image-Based Condition Monitoring of Air-Spinning Machines with Deep Neural Networks
Show others...
2024 (English)In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

Industrial condition monitoring has benefited significantly from developments in machine learning and deep learning. However, textile machines, to a large extent, still use simple sensor systems, requiring additional manual quality inspections. This paper focuses on applying deep neural networks (DNNs) in image-based condition monitoring of air-spinning machines. It specifically focuses on the spinning pressure parameter, which is strongly related to the quality of the produced yarn. The study aims to develop a method to detect structural defects in yarns and assign them to specific machine conditions. DNNs are used to analyze images of yarns generated at different spinning pressures within the spinning box to create a rich dataset for training deep learning models. The study then evaluates the effectiveness of the DNN-based approach in detecting and classifying structural defects in yarns and determining the corresponding machine conditions. The results demonstrate that the developed model can distinguish good yarn from bad yarn, which is used to analyze the proportion of good yarn segments in a longer yarn section. A decreasing proportion with decreasing spinning pressure can thus be used to identify trends in degrading machine conditions. The outcomes of the presented research could potentially help textile enterprises improve the quality and efficiency of their yarn manufacturing processes. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
AI, air-spinning, artificial intelligence, condition monitoring, Deep learning, textile machines
National Category
Materials Engineering
Identifiers
urn:nbn:se:miun:diva-52592 (URN)10.1109/SAS60918.2024.10636697 (DOI)001304520300119 ()2-s2.0-85203698814 (Scopus ID)9798350369250 (ISBN)
Conference
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Available from: 2024-09-24 Created: 2024-09-24 Last updated: 2024-11-25Bibliographically approved
Bader, S. & Oelmann, B. (2024). Instrumentation and Measurement Systems: The Challenge of Designing Energy Harvesting Sensor Systems. IEEE Instrumentation & Measurement Magazine, 27(4), 22-28
Open this publication in new window or tab >>Instrumentation and Measurement Systems: The Challenge of Designing Energy Harvesting Sensor Systems
2024 (English)In: IEEE Instrumentation & Measurement Magazine, ISSN 1094-6969, E-ISSN 1941-0123, Vol. 27, no 4, p. 22-28Article in journal (Refereed) Published
Abstract [en]

With the advent of low-cost and low-power computation, communication and sensor devices, novel instrumentation and measurement applications have been enabled, such as real-time industrial condition monitoring and fine-grained environmental monitoring. In these application scenarios, a lack of available infrastructures for communication and power supply is a common problem. In industrial applications, for example, the machine to be monitored and the monitoring system itself have significantly different technology lifespans, which requires that the monitoring system be retrofitted to machines that are already in use. In environmental monitoring, measurement systems are deployed as standalone devices in potentially remote areas. Consequently, the more autonomous the sensor system can be in terms of required infrastructure, the better it can match application and business needs.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-51461 (URN)10.1109/MIM.2024.10540407 (DOI)001237211600004 ()2-s2.0-85195049128 (Scopus ID)
Available from: 2024-06-11 Created: 2024-06-11 Last updated: 2024-06-14Bibliographically approved
Wu, M., Xu, Y. & Bader, S. (2024). Multi-Phase Variable Reluctance Energy Harvester for Smart Bearing Hub Units. In: 2024 IEEE 23rd International Conference on Micro and Miniature Power Systems, Self-Powered Sensors and Energy Autonomous Devices (PowerMEMS): . Paper presented at 2024 IEEE 23rd International Conference on Micro and Miniature Power Systems, Self-Powered Sensors and Energy Autonomous Devices, PowerMEMS 2024 (pp. 46-49). IEEE conference proceedings
Open this publication in new window or tab >>Multi-Phase Variable Reluctance Energy Harvester for Smart Bearing Hub Units
2024 (English)In: 2024 IEEE 23rd International Conference on Micro and Miniature Power Systems, Self-Powered Sensors and Energy Autonomous Devices (PowerMEMS), IEEE conference proceedings, 2024, p. 46-49Conference paper, Published paper (Refereed)
Abstract [en]

This paper reports on the design, optimization, and evaluation of a multi-phase variable reluctance energy harvester (MP-VREH) for integration into a smart bearing hub unit of large commercial vehicles. The MP-VREH converts rotational kinetic energy into electrical energy to enable the supply of smart electronic systems to monitor the bearing condition. The proposed design consists of a ferromagnetic toothed wheel and six identical m-shaped pickup units. Adhering to the space limitations of an example bearing hub unit, critical parameters, such as the pickup unit pole-piece structure, the magnet height, and the number of teeth, have been optimized using 3D finite element analysis. The optimized harvester provides output powers ranging from 0.46 W to 4.77 W at relevant rotational speeds of 100 to 400 RPM. This is deemed to provide sufficient energy for typical application scenarios. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
Electromagnetic Transducers, Rotational Energy Harvesting, Smart Bearings, Variable Reluctance
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-53782 (URN)10.1109/PowerMEMS63147.2024.10814240 (DOI)2-s2.0-85216512517 (Scopus ID)9798350380200 (ISBN)
Conference
2024 IEEE 23rd International Conference on Micro and Miniature Power Systems, Self-Powered Sensors and Energy Autonomous Devices, PowerMEMS 2024
Available from: 2025-02-11 Created: 2025-02-11 Last updated: 2025-02-11Bibliographically approved
Martinez Rau, L., Chelotti, J. O., Giovanini, L. L., Adin, V., Oelmann, B. & Bader, S. (2024). On-Device Feeding Behavior Analysis of Grazing Cattle. IEEE Transactions on Instrumentation and Measurement, 73, Article ID 2512113.
Open this publication in new window or tab >>On-Device Feeding Behavior Analysis of Grazing Cattle
Show others...
2024 (English)In: IEEE Transactions on Instrumentation and Measurement, ISSN 0018-9456, E-ISSN 1557-9662, Vol. 73, article id 2512113Article in journal (Refereed) Published
Abstract [en]

Precision livestock farming (PLF) leverages cutting-edge technologies and data-driven solutions to enhance the efficiency of livestock production, its associated management, and its welfare. Continuous monitoring of the masticatory sound of cattle allows the estimation of dry-matter intake, classification of jaw movements (JMs), and recognition of grazing and rumination bouts. Over the past two decades, algorithms for analyzing feeding sounds have seen improvements in performance and computational requirements. Nevertheless, in some cases, these algorithms have been implemented on resource-constrained electronic devices, limiting their functionality to one specific task: either classifying JMs or recognizing feeding activities (such as grazing and rumination). In this work, we present an acoustic monitoring system that comprehensively analyzes grazing cattle's feeding behavior at multiple scales. This embedded system classifies different types of JMs, identifies feeding activities, and provides predictor variables for estimating dry-matter intake. Results are transmitted remotely to a base station using long-range communication (LoRa). Two variants of the system have been deployed on a Raspberry Pi Pico board, based on a low-power ARM Cortex-M0+ microcontroller. Both firmware versions make use of direct access memory, sleep mode, and clock-gating techniques to minimize energy consumption. In laboratory experiments, the first deployment consumes 20.1 mW and achieves an F1-score of 87.3% for the classification of JMs and 87.0% for feeding activities. The second deployment consumes 19.1 mW and reaches an F1-score of 84.1% for JMs and 83.5% for feeding activities. The modular design of the proposed embedded monitoring system facilitates integration with energy-harvesting power sources for autonomous operation in field conditions.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Monitoring, Cows, Animals, Acoustics, Microphones, Agriculture, Classification algorithms, Edge computing, embedded machine learning, feeding behavior, microcontroller, on-device processing, precision livestock farming (PLF)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:miun:diva-52041 (URN)10.1109/TIM.2024.3376013 (DOI)001193312100043 ()2-s2.0-85188001389 (Scopus ID)
Available from: 2024-08-07 Created: 2024-08-07 Last updated: 2024-08-07Bibliographically approved
Martinez Rau, L., Zhang, Y., Oelmann, B. & Bader, S. (2024). TinyML Anomaly Detection for Industrial Machines with Periodic Duty Cycles. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Open this publication in new window or tab >>TinyML Anomaly Detection for Industrial Machines with Periodic Duty Cycles
2024 (English)In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

Electro-mechanical systems operating in periodic cycles are pivotal in the Industry 4.0, enabling automated processes that enhance efficiency and productivity. Early detection of failures and anomalies in duty cycles of these machines is crucial to ensure uninterrupted operation and prevent costly downtimes. Although the wear and damage of machines have been extensively studied, a significant proportion of these problems can be traced back to operator errors, underlining the importance of continuously monitoring the machine activity to ensure optimal performance. This work presents an automatic algorithm designed to identify improper duty cycles of industrial machines, exemplified on a mining conveyor belt. To enable the identification of duty cycles, the operational states of the machine are first categorized using machine learning (ML). The study compares six tiny ML techniques on two resource-constrained microcontrollers, reporting an f1-score of 87.6% for identifying normal and abnormal duty cycles and 96.8% for the internal states of the conveyor belt system. Deployed on both low-power microcontrollers, the algorithm processes input data in less than 106 μs, consuming less than 1.16 μJ. These findings promise to facilitate integration into more comprehensive preventive maintenance algorithms. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
anomaly detection, conveyor belt, industry 4.0, low-power microcontroller, machine learning, maintenance, tinyML
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
urn:nbn:se:miun:diva-52585 (URN)10.1109/SAS60918.2024.10636584 (DOI)001304520300086 ()2-s2.0-85203721689 (Scopus ID)9798350369250 (ISBN)
Conference
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Available from: 2024-09-24 Created: 2024-09-24 Last updated: 2024-11-25Bibliographically approved
Trpcheska, A., Zevnik, F. & Bader, S. (2024). Towards Real-Time Vision-Based Sign Language Recognition on Edge Devices. In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings: . Paper presented at 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings. IEEE conference proceedings
Open this publication in new window or tab >>Towards Real-Time Vision-Based Sign Language Recognition on Edge Devices
2024 (English)In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a comparative study focused on the classification of American Sign Language (ASL) gestures and the challenges involved in interpreting the signs for effective communication. Using transfer learning this study evaluates three variants of MobileNet, a machine-learning model optimized for low-resource environments, on a vision-based dataset. The models are deployed on an STM32F746G microcontroller with a Cortex-M7 core. Two frameworks are compared, namely TensorFlow Lite for Microcontrollers and STM32Cube.AI. An ArduCam Mini camera with a maximum image resolution of 5 megapixels is utilized to capture the hand gestures. The study concludes that STM32Cube.AI is the preferred implementation due to its lower model ROM and RAM requirements. Among the three tested models, MobileNetV1 is the most suitable for the task, achieving the highest F1-score of 0.865, the smallest memory footprint of 290.96 kB of ROM and 85.59 kB of RAM, and the shortest inference time of 103 ms. Despite these promising results, the models encountered some difficulties distinguishing between similar signs, highlighting the challenges involved in real-time sign language recognition and the need for further research. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024
Keywords
American sign language, computer vision, embedded machine learning, embedded systems, TinyML, transfer learning
National Category
Computer Systems
Identifiers
urn:nbn:se:miun:diva-52587 (URN)10.1109/SAS60918.2024.10636604 (DOI)001304520300093 ()2-s2.0-85203713331 (Scopus ID)9798350369250 (ISBN)
Conference
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Available from: 2024-09-24 Created: 2024-09-24 Last updated: 2024-11-25Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8382-0359

Search in DiVA

Show all publications