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  • 1.
    Hasanaj, Rilind
    et al.
    Kristianstad University, Faculty of Natural Science.
    Abuhemidan, Ahmed
    Kristianstad University, Faculty of Natural Science.
    Air-quality sensor with 10-years lifespan2019Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Sensors with very low power consumption are required so that they can last a long time without the need to replace the batteries very often. Low power sensors can save significant cost and time incurred in battery replacement, especially in establishments and organizations that span over several buildings, floors and rooms. In this thesis, we investigate the use of the low-power wireless protocol Z-wave for sensors solutions that can last for approximately 10 years. An algorithm was created and we concluded that 10 years on a 480 mAh battery is not possible and the expected years need to be lowered or we need to increase the battery capacity.

  • 2.
    Huang, Xin
    et al.
    Department of Computer Science, Institute for the Future of Computing/Oxford Martin School, University of Oxford.
    Chen, Bangdao
    Department of Computer Science, University of Oxford.
    Markham, Andrew
    Department of Computer Science, Institute for the Future of Computing/Oxford Martin School, University of Oxford.
    Wang, Qinghua
    Kristianstad University, School of Health and Society, Avdelningen för Design och datavetenskap. Department of Communications and Networking, Aalto University, Espoo.
    Zheng, Yan
    Department of Communications and Networking, Aalto University, Espoo.
    Roscoe, Andrew William
    Department of Computer Science, Institute for the Future of Computing/Oxford Martin School, University of Oxford.
    Human interactive secure key and ID exchange protocols in body sensor networks2013In: IET Information Security, ISSN 1751-8709, E-ISSN 1751-8717, Vol. 7, no 1, p. 30-38Article in journal (Refereed)
    Abstract [en]

    A body sensor network (BSN) is typically a wearable wireless sensor network. Security protection is critical to BSNs, since they collect sensitive personal information. Generally speaking, security protection of BSN relies on identity (ID) and key distribution protocols. Most existing protocols are designed to run in general wireless sensor networks, and are not suitable for BSNs. After carefully examining the characteristics of BSNs, the authors propose human interactive empirical channel-based security protocols, which include an elliptic curve Diffie–Hellman version of symmetric hash commitment before knowledge protocol and an elliptic curve Diffie–Hellman version of hash commitment before knowledge protocol. Using these protocols, dynamically distributing keys and IDs become possible. As opposite to present solutions, these protocols do not need any pre-deployment of keys or secrets. Therefore compromised and expired keys or IDs can be easily changed. These protocols exploit human users as temporary trusted third parties. The authors, thus, show that the human interactive channels can help them to design secure BSNs.

  • 3.
    Jin, Shasha
    et al.
    Kristianstad University, School of Health and Society.
    Gaoding, Ningcheng
    Kristianstad University, School of Health and Society.
    Signal processing using the wavelet transform and the Karhunen-Loeve transform2012Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This degree project deals with Wavelet transform and Karhunen-Loeve transform. Through the mathematic description to understand and simulation to investigate the denoise ability of WT and the de-correlation ability of KLT. Mainly prove that the new algorithm which is the joint of these two algorithms is feasible.

  • 4.
    Shala, Ubejd
    et al.
    Kristianstad University, School of Health and Society.
    Rodriguez, Angel
    Kristianstad University, School of Health and Society.
    Indoor Positioning using Sensor-fusion in Android Devices2011Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This project examines the level of accuracy that can be achieved in precision positioning by using built-in sensors in an Android smartphone. The project is focused in estimating the position of the phone inside a building where the GPS signal is bad or unavailable. The approach is sensor-fusion: by using data from the device’s different sensors, such as accelerometer, gyroscope and wireless adapter, the position is determined. The results show that the technique is promising for future handheld indoor navigation systems that can be used in malls, museums, large office buildings, hospitals, etc.

  • 5.
    Song, Qunying
    et al.
    Kristianstad University, School of Health and Society.
    Xu, Jingjing
    Kristianstad University, School of Health and Society.
    Study and Implementation of Patient Data Collection and Presentation for an eHealth Application2013Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This degree project is a part of information and communication technology supported self-care system for the diabetes, mainly in diabetes data collection and visualization. The report is organized in four main sections: investigation and internet search, literature review, application design and implementation, system test and evaluation. Existed applications and research studies has been compared and, a responsive web application is developed aiming at providing relevant functionalities and services regarding diabetes self-management.

  • 6.
    Teljega, Marijana
    Kristianstad University, Faculty of Natural Science, Avdelningen för datavetenskap.
    Automatic Control of a Window Blind using EEG signals2018Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    This thesis uses one of Brain Computer Interface (BCI) products, NeuroSky headset, to design a prototype model to control window blind by using headset’s single channel electrode. Seven volunteers performed eight different exercises while the signal from the headset was recorded. The dataset was analyzed, and exercises with strongest power spectral density (PSD) were chosen to continue to work with. Matlabs spectrogram function was used to divide the signal in time segments, which were 0.25 seconds. One segment from each of these eight exercises was taken to form different combinations which were later classified.The classification result, while using two of proposed exercises (tasks) was successful with 97.0% accuracy computed by Nearest Neighbor classifier. Still, we continued to investigate if we could use three or four thoughts to create three or four commands. The result presented lower classification accuracy when using either 3 or 4 command thoughts with performance accuracy of 92% and 76% respectively.Thus, two or three exercises can be used for constructing two or three different commands.

  • 7.
    Wang, Qinghua
    et al.
    Kristianstad University, School of Health and Society, Avdelningen för Design och datavetenskap.
    Yigitler, Huseyin
    Finland.
    Huang, Xin
    Kina.
    Jäntti, Riku
    Finland.
    Localizing multiple objects using radio tomographic imaging technology2016In: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, no 5, p. 3641-3656Article in journal (Refereed)
    Abstract [en]

    Low data rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio frequency magnetic field, and can be detected by observing the change of radio attenuation. This gives the basis for the radio tomographic imaging technology which has been recently developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multi-object intrusion detection and localization is a challenging issue. This article proposes an extended VB-GMM (i.e. variational Bayesian Gaussian mixture model) algorithm in treating this problem. The extended VBGMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects, and uses a modified version of the variational Bayesian approach for model estimation. Real world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multi-object localization algorithm.

  • 8.
    Wang, Qinghua
    et al.
    Kristianstad University, School of Health and Society, Avdelningen för Design och datavetenskap. Aalto University, Finland.
    Yigitler, Huseyin
    Aalto University, Finland.
    Jäntti, Riku
    Aalto University, Finland.
    System monitoring and fault diagnosis in wireless sensor systems2012In: Aalto University Workshop on Wireless Sensor Systems, WoWSS 2012, 2012Conference paper (Refereed)
    Abstract [en]

    In this paper, we discuss fault diagnosis for wireless sensor systems. Fault diagnosis is only possible if comprehensive system monitoring is in place. We thus also present architecture for in-depth system monitoring.

  • 9.
    Zhou, Xujia
    et al.
    Kristianstad University, Faculty of Natural Science, Avdelningen för datavetenskap.
    Flora, Eduardo
    Kristianstad University, Faculty of Natural Science, Avdelningen för datavetenskap.
    Comparison of Performance and Power Consumption Between GPS and Sigfox Positioning Using Pycom Modules2018Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Sigfox is one of the newly-emerging LPWAN (Low Power Wide Area Network) technologies aiming to provide power-efficient solutions to the world of IoT. This study presents a comparison between Sigfox Geolocation and GPS (Global Positioning System) in terms of power consumption and performance which includes three metrics: accuracy and precision, response rate and response time. This study includes for the first part a series of lab tests where Sigfox Geolocation and GPS were studied in a single Sleep, Wake up, Idle, Tx/Rx cycle. For the second part, field tests with different geographical parameters (altitude, population, mobility) were conducted. Results of lab tests show that power consumption difference between Sigfox and GPS is a linear function of Idle time. In field tests, GPS presents a far superior performance than Sigfox in all metrics and marginally better power efficiency for relatively short Idle interval. For both GPS and Sigfox, a correlation between power efficiency and performance was observed. Results show that GPS operates best in rural environments while Sigfox stands out in urban ones. Payload size was observed to affect Sigfox in both power consumption and performance while different transmission rates only affect power consumption but do not seem to impact the other metrics. A solution based on the outcome of this study is suggested for a freight-monitoring system where geolocation is handled by GPS and the coordinates transmitted via Sigfox. As an emerging technology under constant development, Sigfox Geolocation is expected to have improved performance in the near future.

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