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  • 1.
    Abdullah, Saad
    et al.
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Hafid, Abdelakram
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Folke, Mia
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Lindén, Maria
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Kristoffersson, Annica
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    A Novel Fiducial Point Extraction Algorithm to Detect C and D Points from the Acceleration Photoplethysmogram (CnD)2023Inngår i: Electronics, E-ISSN 2079-9292, Vol. 12, nr 5, artikkel-id 1174Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The extraction of relevant features from the photoplethysmography signal for estimating certain physiological parameters is a challenging task. Various feature extraction methods have been proposed in the literature. In this study, we present a novel fiducial point extraction algorithm to detect c and d points from the acceleration photoplethysmogram (APG), namely “CnD”. The algorithm allows for the application of various pre-processing techniques, such as filtering, smoothing, and removing baseline drift; the possibility of calculating first, second, and third photoplethysmography derivatives; and the implementation of algorithms for detecting and highlighting APG fiducial points. An evaluation of the CnD indicated a high level of accuracy in the algorithm’s ability to identify fiducial points. Out of 438 APG fiducial c and d points, the algorithm accurately identified 434 points, resulting in an accuracy rate of 99%. This level of accuracy was consistent across all the test cases, with low error rates. These findings indicate that the algorithm has a high potential for use in practical applications as a reliable method for detecting fiducial points. Thereby, it provides a valuable new resource for researchers and healthcare professionals working in the analysis of photoplethysmography signals.

    Fulltekst (pdf)
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  • 2.
    Azeem, Muhammad
    et al.
    University of Engineering and Technology, Pakistan.
    Malik, Tahir Nadeem
    HITECH University, Pakistan.
    Muqeet, Hafiz Abdul
    Punjab Tianjin University of Technology Lahore, Pakistan.
    Hussain, Muhammad Majid
    Heriot-Watt University, United Kingdom.
    Ali, Ahmad
    Bahria University, Pakistan.
    Khan, Baber
    International Islamic University, Pakistan.
    Rehman, Atiq Ur
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Combined Economic Emission Dispatch in Presence of Renewable Energy Resources Using CISSA in a Smart Grid Environment2023Inngår i: Electronics, E-ISSN 2079-9292, Vol. 12, nr 3, artikkel-id 715Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The geographically spatial and controlled distribution of fossil fuel resources, catastrophic global warming, and depletion of fossil fuel resources have forced us to integrate zero- or low-emissions energy resources, such as wind and solar, in the generation mix. These renewable energy resources are unexhausted, available around the globe, and free of cost. The advancement in wind and solar technologies has caused an appreciable decrease in installed the and global levelized costs of electricity via these sources. Therefore, the penetration of renewable energy resources in the generation mix can provide a promising solution to the above-mentioned problems. The aim of simultaneously reducing fuel consumption in terms of “Fuel Cost” and “Emission” in thermal power plants is called a combined economic emission dispatch problem. It is a combinatorial and multi-objective optimization problem. The solution of this problem is to allocate the load demand and losses on the committed units in such way that the overall costs of the generation and emission of thermal units are reduced, while the legal bounds (constraints) are met. It is a highly non-linear and complex optimization problem. The valve-point loading effect makes this problem non-convex. The addition of renewable energy resources (RERs) adds more complexities to this problem because they are intermittent. In this work, chaotic salp swarm algorithms (CISSA) are used to solve the combined economic emission dispatch problem. Chaos is used as an alternative to randomization for the tuning of the control variable to improve the trait of obtaining global extrema. Different test cases having different combinations of thermal, solar, and wind units are solved using the proposed algorithm. The results show the superiority of this study in comparison to the existent research results in terms of the cost of generation and emissions.

  • 3.
    Bibbo, D.
    et al.
    Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, 00154, Italy.
    Mariajoseph, M.
    Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, 00154, Italy.
    Gallina, Barbara
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system.
    Carli, M.
    Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, 00154, Italy.
    A Novel Physiological-Based System to Assess Drivers’ Stress during Earth Moving Simulated Activities2022Inngår i: Electronics, E-ISSN 2079-9292, Vol. 11, nr 24, artikkel-id 4074Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Earth-moving vehicles (EMVs) are vital in numerous industries, including construction, forestry, mining, cleaning, and agriculture. The changing nature of the off-road environment in which they operate makes situational awareness for readiness and, consequently, mental stress crucial for drivers and requires a high level of controllability. Therefore, the monitoring of drivers’ acute stress patterns may be used as an input in identifying various levels of attentiveness. This research presents an experimental evaluation of a physiological-based system that can be useful to evaluate the readiness of a driver in different conditions. For the experimental validation, physiological signals such as electrocardiogram (ECG), galvanic skin response (GSR) and speech data were collected from nine participants throughout driving experiments of increasing complexity on a specific simulator. The experimental results show that the identified parameters derived from the acquired physiological signals can help us understand the driver status when performing different tasks, the engagement of which is related to different road environments. This multi-parameter approach can provide more reliable information compared to single parameter approaches (e.g., eye monitoring with a camera) and identify driver status variations, from relaxed to stressed or drowsy. The use of these signals allows for the development of a smart driving cockpit, which could communicate to the vehicle the driver’s status, to set up an innovative protection system aiming to increase road safety. 

  • 4.
    Elahe, M. F.
    et al.
    Department of Software Engineering, Daffodil International University, Dhaka 1216, Bangladesh.
    Kabir, Md Alamgir
    Mälardalens universitet, Akademin för innovation, design och teknik, Inbyggda system. Centre for Advanced Machine Learning and Application (CAMLAs), Dhaka 1229, Bangladesh.
    Mahmud, S. M. H.
    Centre for Advanced Machine Learning and Application (CAMLAs), Dhaka 1229, Bangladesh.
    Azim, R.
    College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China.
    Factors Impacting Short-Term Load Forecasting of Charging Station to Electric Vehicle2023Inngår i: Electronics, E-ISSN 2079-9292, Vol. 12, nr 1Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The rapid growth of electric vehicles (EVs) is likely to endanger the current power system. Forecasting the demand for charging stations is one of the critical issues while mitigating challenges caused by the increased penetration of EVs. Uncovering load-affecting features of the charging station can be beneficial for improving forecasting accuracy. Existing studies mostly forecast electricity demand of charging stations based on load profiling. It is difficult for public EV charging stations to obtain features for load profiling. This paper examines the power demand of two workplace charging stations to address the above-mentioned issue. Eight different types of load-affecting features are discussed in this study without compromising user privacy. We found that the workplace EV charging station exhibits opposite characteristics to the public EV charging station for some factors. Later, the features are used to design the forecasting model. The average accuracy improvement with these features is 42.73% in terms of RMSE. Moreover, the experiments found that summer days are more predictable than winter days. Finally, a state-of-the-art interpretable machine learning technique has been used to identify top contributing features. As the study is conducted on a publicly available dataset and analyzes the root cause of demand change, it can be used as baseline for future research.

  • 5.
    Pozo Pérez, Francisco Manuel
    et al.
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Rodriguez-Navas, Guillermo
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Hansson, Hans
    Mälardalens högskola, Akademin för innovation, design och teknik, Inbyggda system.
    Methods for large-scale time-triggered network scheduling2019Inngår i: Electronics, E-ISSN 2079-9292, Vol. 8, nr 7, artikkel-id 738Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Future cyber–physical systems may extend over broad geographical areas, like cities or regions, thus, requiring the deployment of large real-time networks. A strategy to guarantee predictable communication over such networks is to synthesize an offline time-triggered communication schedule. However, this synthesis problem is computationally hard (NP-complete), and existing approaches do not scale satisfactorily to the required network sizes. This article presents a segmented offline synthesis method which substantially reduces this limitation, being able to generate time-triggered schedules for large hybrid (wired and wireless) networks. We also present a series of algorithms and optimizations that increase the performance and compactness of the obtained schedules while solving some of the problems inherent to segmented approaches. We evaluate our approach on a set of realistic large-size multi-hop networks, significantly larger than those considered in the existing literature. The results show that our segmentation reduces the synthesis time by up to two orders of magnitude.

  • 6.
    Zolfaghari, Samaneh
    et al.
    Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy.
    Massa, Silvia M.
    Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy.
    Riboni, Daniele
    Department of Mathematics and Computer Science, University of Cagliari, Via Ospedale 72, 09124 Cagliari, Italy.
    Activity Recognition in Smart Homes via Feature-Rich Visual Extraction of Locomotion Traces2023Inngår i: Electronics, E-ISSN 2079-9292, Vol. 12, nr 9, s. 1969-1969Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The proliferation of sensors in smart homes makes it possible to monitor human activities, routines, and complex behaviors in an unprecedented way. Hence, human activity recognition has gained increasing attention over the last few years as a tool to improve healthcare and well-being in several applications. However, most existing activity recognition systems rely on cameras or wearable sensors, which may be obtrusive and may invade the user’s privacy, especially at home. Moreover, extracting expressive features from a stream of data provided by heterogeneous smart-home sensors is still an open challenge. In this paper, we investigate a novel method to detect activities of daily living by exploiting unobtrusive smart-home sensors (i.e., passive infrared position sensors and sensors attached to everyday objects) and vision-based deep learning algorithms, without the use of cameras or wearable sensors. Our method relies on depicting the locomotion traces of the user and visual clues about their interaction with objects on a floor plan map of the home, and utilizes pre-trained deep convolutional neural networks to extract features for recognizing ongoing activity. One additional advantage of our method is its seamless extendibility with additional features based on the available sensor data. Extensive experiments with a real-world dataset and a comparison with state-of-the-art approaches demonstrate the effectiveness of our method.

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