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  • 1. Asan, Noor Badariah
    et al.
    Hassan, Emadeldeen
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Perez, Mauricio David
    Shah, Syaiful Redzwan Mohd
    Velander, Jacob
    Blokhuis, Taco J.
    Voigt, Thiemo
    Augustine, Robin
    Assessment of Blood Vessel Effect on Fat-Intrabody Communication Using Numerical and Ex-Vivo Models at 2.45 GHZ2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 89886-89900Article in journal (Refereed)
    Abstract [en]

    The potential offered by the intra-body communication (IBC) over the past few years has resulted in a spike of interest for the topic, specifically for medical applications. Fat-IBC is subsequently a novel alternative technique that utilizes fat tissue as a communication channel. This work aimed to identify such transmission medium and its performance in varying blood-vessel systems at 2.45 GHz, particularly in the context of the IBC and medical applications. It incorporated three-dimensional (3D) electromagnetic simulations and laboratory investigations that implemented models of blood vessels of varying orientations, sizes, and positions. Such investigations were undertaken by using ex-vivo porcine tissues and three blood-vessel system configurations. These configurations represent extreme cases of real-life scenarios that sufficiently elucidated their principal influence on the transmission. The blood-vessel models consisted of ex-vivo muscle tissues and copper rods. The results showed that the blood vessels crossing the channel vertically contributed to 5.1 dB and 17.1 dB signal losses for muscle and copper rods, respectively, which is the worst-case scenario in the context of fat-channel with perturbance. In contrast, blood vessels aligned-longitudinally in the channel have less effect and yielded 4.5 dB and 4.2 dB signal losses for muscle and copper rods, respectively. Meanwhile, the blood vessels crossing the channel horizontally displayed 3.4 dB and 1.9 dB signal losses for muscle and copper rods, respectively, which were the smallest losses among the configurations. The laboratory investigations were in agreement with the simulations. Thus, this work substantiated the fat-IBC signal transmission variability in the context of varying blood vessel configurations.

  • 2. De Alcantara Dias, Bruno Martin
    et al.
    Maria Lagana, Armando Antonio
    Justo, Joao Francisco
    Yoshioka, Leopoldo Rideki
    Dias Santos, Max Mauro
    Gu, Zonghua
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Model-based development of an engine control module for a spark ignition engine2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 53638-53649Article in journal (Refereed)
    Abstract [en]

    A Spark ignition (SI) engine is a complex, multi-domain component of the vehicle powertrain system. The engine control module (ECM) for an SI engine must achieve both high performance and good fuel efficiency. In this paper, we present a model-based development methodology for an open architecture ECM, addressing the entire development lifecycle including a control algorithm design, parameter calibration, hardware/software implementation, and verification/validation of the final system, both with bench tests on a dynamometer and in a real vehicle on the road. The ECM is able to achieve similar performance as the original proprietary ECM provided by the original equipment manufacturer. Its flexible and modular design enables easy extensibility with new control algorithms, and development of new engine types.

  • 3. Kleyko, Denis
    et al.
    Osipov, Evgeny
    Wiklund, Urban
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    A Hyperdimensional Computing Framework for Analysis of Cardiorespiratory Synchronization During Paced Deep Breathing2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 34403-34415Article in journal (Refereed)
    Abstract [en]

    Autonomic function during deep breathing (DB) is normally scored based on the assumption that the heart rate is synchronized with the breathing. We have observed individuals with subtle arrhythmias during DB, where an autonomic function cannot be evaluated. This paper presents a novel method for analyzing cardiorespiratory synchronization: feature-based analysis of the similarity between heart rate and respiration using the principles of hyperdimensional computing. Heart rate and respiration signals were modeled using Fourier series analysis. Three feature variables were derived and mapped to binary vectors in a high-dimensional space. Using both synthesized data and recordings from patients/healthy subjects, the similarity between the feature vectors was assessed using Hamming distance (high-dimensional space), Euclidean distance (original space), and with a coherence-based index. Methods were evaluated via the classification of the similarity indices into three groups. The distance-based methods achieved good separation of signals into classes with different degrees of cardiorespiratory synchronization, also providing identification of patients with low cardiorespiratory synchronization but high values of conventional DB scores. Moreover, binary high-dimensional vectors allowed an additional analysis of the obtained Hamming distance. Feature-based similarity analysis using hyperdimensional computing is capable of identifying signals with low cardiorespiratory synchronization during DB due to arrhythmias. Vector-based similarity analysis could be applied to other types of feature variables than based on spectral analysis. The proposed methods for robustly assessing cardiorespiratory synchronization during DB facilitate the identification of individuals where the evaluation of the autonomic function is problematic or even impossible, thus increasing the correctness of the conventional DB scores.

  • 4.
    Rezk, Nesma
    et al.
    Halmstad University.
    Purnaprajna, Madhura
    Amrita School of Engineering: Bangalore, Karnataka, India.
    Nordström, Tomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Ul-Abdin, Zain
    Halmstad University.
    Recurrent Neural Networks: An Embedded Computing Perspective2020In: IEEE Access, E-ISSN 2169-3536, Vol. 81, no 1, p. 57967-57996Article in journal (Refereed)
    Abstract [en]

    Recurrent Neural Networks (RNNs) are a class of machine learning algorithms used for applications with time-series and sequential data. Recently, there has been a strong interest in executing RNNs on embedded devices. However, difficulties have arisen because RNN requires high computational capability and a large memory space. In this paper, we review existing implementations of RNN models on embedded platforms and discuss the methods adopted to overcome the limitations of embedded systems. We will define the objectives of mapping RNN algorithms on embedded platforms and the challenges facing their realization. Then, we explain the components of RNN models from an implementation perspective. We also discuss the optimizations applied to RNNs to run efficiently on embedded platforms. Finally, we compare the defined objectives with the implementations and highlight some open research questions and aspects currently not addressed for embedded RNNs. Overall, applying algorithmic optimizations to RNN models and decreasing the memory access overhead is vital to obtain high efficiency. To further increase the implementation efficiency, we point up the more promising optimizations that could be applied in future research. Additionally, this article observes that high performance has been targeted by many implementations, while flexibility has, as yet, been attempted less often. Thus, the article provides some guidelines for RNN hardware designers to support flexibility in a better manner.

  • 5.
    Rohlén, Robin
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Stålberg, Erik
    Department of Clinical Neurophysiology, Department of Neurosciences, University Hospital, Uppsala University, Sweden.
    Stoverud, Karen-Helene
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Grönlund, Christer
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    A Method for Identification of Mechanical Response of Motor Units in Skeletal Muscle Voluntary Contractions using Ultrafast Ultrasound Imaging: Simulations and Experimental Tests2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 50299-50311Article in journal (Refereed)
    Abstract [en]

    The central nervous system coordinates movement through forces generated by motor units (MUs) in skeletal muscles. To analyze MUs function is essential in sports, rehabilitation medicine applications, and neuromuscular diagnostics. The MUs and their function are studied using electromyography. Typically, these methods study only a small muscle volume (1 mm3) or only a superficial (< 1 cm) volume of the muscle. Here we introduce a method to identify so-called mechanical units, i.e., the mechanical response of electrically active MUs, in the whole muscle (4x4 cm, cross-sectional) under voluntary contractions by ultrafast ultrasound imaging and spatiotemporal decomposition. We evaluate the performance of the method by simulation of active MUs’ mechanical response under weak contractions. We further test the experimental feasibility on eight healthy subjects. We show the existence of mechanical units that contribute to the tissue dynamics in the biceps brachii at low force levels and that these units are similar to MUs described by electromyography with respect to the number of units, territory sizes, and firing rates. This study introduces a new potential neuromuscular functional imaging method, which could be used to study a variety of questions on muscle physiology that previously were difficult or not possible to address.

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