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  • 1. Badariah Asan, Noor
    et al.
    Hassan, Emadeldeen
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Department of Electronics and Electrical Communications, Menoufia University, Menouf, Egypt.
    Velander, Jacob
    Redzwan Mohd Shah, Syaiful
    Noreland, Daniel
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Blokhuis, Taco J.
    Wadbro, Eddie
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Berggren, Martin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Voigt, Thiemo
    Augustine, Robin
    Characterization of the Fat Channel for Intra-Body Communication at R-Band Frequencies2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 9, article id 2752Article in journal (Refereed)
    Abstract [en]

    In this paper, we investigate the use of fat tissue as a communication channel between in-body, implanted devices at R-band frequencies (1.7–2.6 GHz). The proposed fat channel is based on an anatomical model of the human body. We propose a novel probe that is optimized to efficiently radiate the R-band frequencies into the fat tissue. We use our probe to evaluate the path loss of the fat channel by studying the channel transmission coefficient over the R-band frequencies. We conduct extensive simulation studies and validate our results by experimentation on phantom and ex-vivo porcine tissue, with good agreement between simulations and experiments. We demonstrate a performance comparison between the fat channel and similar waveguide structures. Our characterization of the fat channel reveals propagation path loss of ∼0.7 dB and ∼1.9 dB per cm for phantom and ex-vivo porcine tissue, respectively. These results demonstrate that fat tissue can be used as a communication channel for high data rate intra-body networks.

  • 2.
    Cheng, Xiaogang
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China; School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden.
    Yang, Bin
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi'an, China.
    Liu, Guoqing
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Li, Haibo
    A total bounded variation approach to low visibility estimation on expressways2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 2, article id 392Article in journal (Refereed)
    Abstract [en]

    Low visibility on expressways caused by heavy fog and haze is a main reason for traffic accidents. Real-time estimation of atmospheric visibility is an effective way to reduce traffic accident rates. With the development of computer technology, estimating atmospheric visibility via computer vision becomes a research focus. However, the estimation accuracy should be enhanced since fog and haze are complex and time-varying. In this paper, a total bounded variation (TBV) approach to estimate low visibility (less than 300 m) is introduced. Surveillance images of fog and haze are processed as blurred images (pseudo-blurred images), while the surveillance images at selected road points on sunny days are handled as clear images, when considering fog and haze as noise superimposed on the clear images. By combining image spectrum and TBV, the features of foggy and hazy images can be extracted. The extraction results are compared with features of images on sunny days. Firstly, the low visibility surveillance images can be filtered out according to spectrum features of foggy and hazy images. For foggy and hazy images with visibility less than 300 m, the high-frequency coefficient ratio of Fourier (discrete cosine) transform is less than 20%, while the low-frequency coefficient ratio is between 100% and 120%. Secondly, the relationship between TBV and real visibility is established based on machine learning and piecewise stationary time series analysis. The established piecewise function can be used for visibility estimation. Finally, the visibility estimation approach proposed is validated based on real surveillance video data. The validation results are compared with the results of image contrast model. Besides, the big video data are collected from the Tongqi expressway, Jiangsu, China. A total of 1,782,000 frames were used and the relative errors of the approach proposed are less than 10%.

  • 3.
    Grip, Helena
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Nilsson, Kjell G
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences.
    Häger, Charlotte G.
    Umeå University, Faculty of Medicine, Department of Community Medicine and Rehabilitation, Section of Physiotherapy.
    Lundström, Ronnie
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Öhberg, Fredrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Does the Femoral Head Size in Hip Arthroplasty Influence Lower Body Movements during Squats, Gait and Stair Walking?: A Clinical Pilot Study Based on Wearable Motion Sensors2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 14, article id 3240Article in journal (Refereed)
    Abstract [en]

    A hip prosthesis design with larger femoral head size may improve functional outcomes compared to the conventional total hip arthroplasty (THA) design. Our aim was to compare the range of motion (RoM) in lower body joints during squats, gait and stair walking using a wearable movement analysis system based on inertial measurement units (IMUs) in three age-matched male groups: 6 males with a conventional THA (THAC), 9 with a large femoral head (LFH) design, and 8 hip- and knee-asymptomatic controls (CTRL). We hypothesized that the LFH design would allow a greater hip RoM, providing movement patterns more like CTRL, and a larger side difference in hip RoM in THAC when compared to LFH and controls. IMUs were attached to the pelvis, thighs and shanks during five trials of squats, gait, and stair ascending/descending performed at self-selected speed. THAC and LFH participants completed the Hip dysfunction and Osteoarthritis Outcome Score (HOOS). The results showed a larger hip RoM during squats in LFH compared to THAC. Side differences in LFH and THAC groups (operated vs. non-operated side) indicated that movement function was not fully recovered in either group, further corroborated by non-maximal mean HOOS scores (LFH: 83 +/- 13, THAC: 84 +/- 19 groups, vs. normal function 100). The IMU system may have the potential to enhance clinical movement evaluations as an adjunct to clinical scales.

  • 4. Huang, Xiao-Yu
    et al.
    Li, Wubin
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Chen, Kang
    Xiang, Xian-Hong
    Pan, Rong
    Li, Lei
    Cai, Wen-Xue
    Multi-Matrices Factorization with Application to Missing Sensor Data Imputation2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 11, p. 15172-15186Article in journal (Refereed)
    Abstract [en]

    We formulate a multi-matrices factorization model (MMF) for the missing sensor data estimation problem. The estimation problem is adequately transformed into a matrix completion one. With MMF, an n-by-t real matrix, R, is adopted to represent the data collected by mobile sensors from n areas at the time, T-1, T-2, . . . , T-t, where the entry, R-i,R-j, is the aggregate value of the data collected in the ith area at T-j. We propose to approximate R by seeking a family of d-by-n probabilistic spatial feature matrices, U-(1), U-(2), . . . , U-(t), and a probabilistic temporal feature matrix, V epsilon R-dxt, where R-j approximate to U-(j)(T) T-j. We also present a solution algorithm to the proposed model. We evaluate MMF with synthetic data and a real-world sensor dataset extensively. Experimental results demonstrate that our approach outperforms the state-of-the-art comparison algorithms.

  • 5. Jiang, Ruyuan
    et al.
    Liu, Niantao
    Gao, Sanshuang
    Mamat, Xamxikamar
    Su, Yuhong
    Wågberg, Thomas
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Li, Yongtao
    Hu, Xun
    Hu, Guangzhi
    A Facile Electrochemical Sensor Based on PyTS-CNTs for Simultaneous Determination of Cadmium and Lead Ions2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 5, article id 1567Article in journal (Refereed)
    Abstract [en]

    A simple and easy method was implemented for the contemporary detection of cadmium (Cd2+) and lead (Pb2+) ions using 1,3,6,8-pyrenetetrasulfonic acid sodium salt-functionalized carbon nanotubes nanocomposites (PyTS-CNTs). The morphology and composition of the obtained PyTS-CNTs were characterized using scanning electron microscopy (SEM), energy dispersive spectrometry (EDS), and X-ray photoelectron spectroscopy (XPS). The experimental results confirmed that the fabricated PyTS-CNTs exhibited good selectivity and sensitivity for metal ion-sensing owing to the insertion of sulfonic acid groups. For Cd2+ and Pb2+, some of the electrochemical sensing parameters were evaluated by varying data such as the PyTS-CNT quantity loaded on the pyrolytic graphite electrode (PGE), pH of the acetate buffer, deposition time, and deposition potential. These parameters were optimized with differential pulse anodic sweeping voltammetry (DPASV). Under the optimal condition, the stripping peak current of the PyTS-CNTs/Nafion/PGE varies linearly with the heavy metal ion concentration, ranging from 1.0 mu g L-1 to 90 mu g L-1 for Cd2+ and from 1.0 mu g L-1 to 110 mu g L-1 for Pb2+. The limits of detection were estimated to be approximately 0.8 mu g L-1 for Cd2+ and 0.02 mu g L-1 for Pb2+. The proposed PyTS CNTs/Nafion/PGE can be used as a rapid, simple, and controllable electrochemical sensor for the determination of toxic Cd2+ and Pb2+.

  • 6. Karpenko, Anastasiia
    et al.
    Kinnunen, Tuomas
    Madhikermi, Manik
    Robert, Jeremy
    Främling, Kary
    Department of Computer Science, Aalto University, 02150 Espoo, Finland.
    Dave, Bhargav
    Nurminen, Antti
    Data Exchange Interoperability in IoT Ecosystem for Smart Parking and EV Charging2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 12Article in journal (Refereed)
    Abstract [en]

    Many domains are trying to integrate with the Internet of Things (IoT) ecosystem, such as public administrations starting smart city initiatives all over the world. Cities are becoming smart in many ways: smart mobility, smart buildings, smart environment and so on. However, the problem of non-interoperability in the IoT hinders the seamless communication between all kinds of IoT devices. Different domain specific IoT applications use different interoperability standards. These standards are usually not interoperable with each other. IoT applications and ecosystems therefore tend to use a vertical communication model that does not allow data sharing horizontally across different IoT ecosystems. In 2014, The Open Group published two domain-independent IoT messaging standards, O-MI and O-DF, aiming to solve the interoperability problem. In this article we describe the practical use of O-MI/O-DF standards for reaching interoperability in a mobile application for the smart city context, in particular for the Smart Mobility domain, electric vehicle (EV) charging case study. The proof-of-concept of the smart EV charging ecosystem with mobile application user interface was developed as a part of an EU (Horizon 2020) Project bIoTope.

  • 7.
    Ostovar, Ahmad
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Bensch, Suna
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Hellström, Thomas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Natural Language Guided Object Retrieval in Images2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220Article in journal (Refereed)
    Abstract [en]

    In this paper we propose a method for generation of responses to natural language queries regarding objects and their spatial relations in given images. The responses comprise identification of objects in the image, and generation of appropriate text answering the query. The proposed method uses a pre-defined neural network (YOLO) for object detection, combined with natural language processing of the given queries. Probabilistic measures are constructed for object classes, spatial relations, and word similarity such that the most likely grounding of the query can be done. By computing semantic similarity, our method overcame the problems with a limited number of object classes in pre-trained network models. At the same time, flexibility regarding the varying ways users express spatial relations was achieved. The method was implemented, and evaluated by 30 test users who considered 81.9\% of the generated answers as correct. The work may be applied in applications where visual input (images or video) and natural language input (speech or text) have to be related to each other. For example, processing of videos may benefit from functionality that relates audio to visual content. Urban Search and Rescue Robots (USAR) are used to find people in catastrophic situations such as flooding or earthquakes. It would be very beneficial if such a robot is able to respond to verbal questions from the operator about what the robot sees with its remote cameras.

  • 8.
    Ostovar, Ahmad
    et al.
    Umeå University, Faculty of Science and Technology, Department of Computing Science. Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431 Ås, Norway.
    Talbot, Bruce
    Puliti, Stefano
    Astrup, Rasmus
    Ringdahl, Ola
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Detection and classification of Root and Butt-Rot (RBR) in Stumps of Norway Spruce Using RGB Images and Machine Learning2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 7, article id 1579Article in journal (Refereed)
    Abstract [en]

    Root and butt-rot (RBR) has a significant impact on both the material and economic outcome of timber harvesting, and therewith on the individual forest owner and collectively on the forest and wood processing industries. An accurate recording of the presence of RBR during timber harvesting would enable a mapping of the location and extent of the problem, providing a basis for evaluating spread in a climate anticipated to enhance pathogenic growth in the future. Therefore, a system to automatically identify and detect the presence of RBR would constitute an important contribution to addressing the problem without increasing workload complexity for the machine operator. In this study, we developed and evaluated an approach based on RGB images to automatically detect tree stumps and classify them as to the absence or presence of rot. Furthermore, since knowledge of the extent of RBR is valuable in categorizing logs, we also classify stumps into three classes of infestation; rot = 0%, 0% < rot > 50% and rot ≥ 50%. In this work we used deep-learning approaches and conventional machine-learning algorithms for detection and classification tasks. The results showed that tree stumps were detected with precision rate of 95% and recall of 80%. Using only the correct output (TP) of the stump detector, stumps without and with RBR were correctly classified with accuracy of 83.5% and 77.5%, respectively. Classifying rot into three classes resulted in 79.4%, 72.4%, and 74.1% accuracy for stumps with rot = 0%, 0% < rot > 50% and rot ≥ 50%, respectively. With some modifications, the developed algorithm could be used either during the harvesting operation to detect RBR regions on the tree stumps or as an RBR detector for post-harvest assessment of tree stumps and logs.

  • 9. Robert, Jérémy
    et al.
    Kubler, Sylvain
    Kolbe, Niklas
    Cerioni, Alessandro
    Gastaud, Emmanuel
    Främling, Kary
    School of Science and Technology, Aalto University, P.O. Box 15500, Aalto 00076, Finland.
    Open IoT ecosystem for enhanced interoperability in smart cities - Example of Métropole de Lyon2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 12, article id 2849Article in journal (Refereed)
    Abstract [en]

    The Internet of Things (IoT) has promised a future where everything gets connected. Unfortunately, building a single global ecosystem of Things that communicate with each other seamlessly is virtually impossible today. The reason is that the IoT is essentially a collection of isolated “Intranets of Things”, also referred to as “vertical silos”, which cannot easily and efficiently interact with each other. Smart cities are perhaps the most striking examples of this problem since they comprise a wide range of stakeholders and service providers who must work together, including urban planners, financial organisations, public and private service providers, telecommunication providers, industries, citizens, and so forth. Within this context, the contribution of this paper is threefold: (i) discuss business and technological implications as well as challenges of creating successful open innovation ecosystems, (ii) present the technological building blocks underlying an IoT ecosystem developed in the framework of the EU Horizon 2020 programme, (iii) present a smart city pilot (Heat Wave Mitigation in Métropole de Lyon) for which the proposed ecosystem significantly contributes to improving interoperability between a number of system components, and reducing regulatory barriers for joint service co-creation practices.

  • 10. Zhao, Gang
    et al.
    Tan, Wei
    Jia, Mengyuan
    Hou, Jiajuan
    Ma, Weiguang
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Dong, Lei
    Zhang, Lei
    Feng, Xiaoxia
    Wu, Xuechun
    Yin, Wangbao
    Xiao, Liantuan
    Axner, Ove
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Jia, Suotang
    Intensity-Stabilized Fast-Scanned Direct Absorption Spectroscopy Instrumentation Based on a Distributed Feedback Laser with Detection Sensitivity down to 4 x 10(-6)2016In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 9, article id 1544Article in journal (Refereed)
    Abstract [en]

    A novel, intensity-stabilized, fast-scanned, direct absorption spectroscopy (IS-FS-DAS) instrumentation, based on a distributed feedback (DFB) diode laser, is developed. A fiber-coupled polarization rotator and a fiber-coupled polarizer are used to stabilize the intensity of the laser, which significantly reduces its relative intensity noise (RIN). The influence of white noise is reduced by fast scanning over the spectral feature (at 1 kHz), followed by averaging. By combining these two noise-reducing techniques, it is demonstrated that direct absorption spectroscopy (DAS) can be swiftly performed down to a limit of detection (LOD) (1 sigma) of 4 x 10(-6), which opens up a number of new applications.

  • 11.
    Åstrand, Anders
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Andersson, Britt
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Jalkanen, Ville
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Börje, Ljungberg
    Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Urology and Andrology.
    Anders, Bergh
    Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
    Lindahl, Olof
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. Umeå University, Faculty of Science and Technology, Centre for Biomedical Engineering and Physics (CMTF).
    Prostate cancer detection with a tactile resonance sensor: measurement considerations and clinical setup2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, article id 2453Article in journal (Refereed)
    Abstract [en]

    Tumors in the human prostate are usually stiffer compared to surrounding non-malignant glandular tissue, and tactile resonance sensors measuring stiffness can be used to detect prostate cancer. To explore this further, we used a tactile resonance sensor system combined with a rotatable sample holder where whole surgically removed prostates could be attached to detect tumors on, and beneath, the surface ex vivo. Model studies on tissue phantoms made of silicone and porcine tissue were performed. Finally, two resected human prostate glands were studied. Embedded stiff silicone inclusions placed 4 mm under the surface could be detected in both the silicone and biological tissue models, with a sensor indentation of 0.6 mm. Areas with different amounts of prostate cancer (PCa) could be distinguished from normal tissue (p < 0.05), when the tumor was located in the anterior part, whereas small tumors located in the dorsal aspect were undetected. The study indicates that PCa may be detected in a whole resected prostate with an uneven surface and through its capsule. This is promising for the development of a clinically useful instrument to detect prostate cancer during surgery.

  • 12.
    Öhberg, Fredrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Bäcklund, Tomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Sundström, Nina
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Grip, Helena
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Portable Sensors Add Reliable Kinematic Measures to the Assessment of Upper Extremity Function2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 5, article id 1241Article in journal (Refereed)
    Abstract [en]

    Ordinal scales with low resolution are used to assess arm function in clinic. These scales may be improved by adding objective kinematic measures. The aim was to analyze within-subject, inter-rater and overall reliability (i.e., including within-subject and inter-rater reliability) and check the system's validity of kinematic measures from inertial sensors for two such protocols on one person. Twenty healthy volunteers repeatedly performed two tasks, finger-to-nose and drinking, during two test sessions with two different raters. Five inertial sensors, on the forearms, upper arms and xiphoid process were used. Comparisons against an optical camera system evaluated the measurement validity. Cycle time, range of motion (ROM) in shoulder and elbow were calculated. Bland-Altman plots and linear mixed models including the generalizability (G) coefficient evaluated the reliability of the measures. Within-subject reliability was good to excellent in both tests (G = 0.80-0.97) and may serve as a baseline when assessing upper extremities in future patient groups. Overall reliability was acceptable to excellent (G = 0.77-0.94) for all parameters except elbow axial rotation in finger-to-nose task and both elbow axial rotation and flexion/extension in drinking task, mainly due to poor inter-rater reliability in these parameters. The low to good reliability for elbow ROM probably relates to high within-subject variability. The sensors provided good to excellent measures of cycle time and shoulder ROM in non-disabled individuals and thus have the potential to improve today's assessment of arm function.

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