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            "63": {
                "pageid": 63,
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                "title": "Reading The GSM Beacon Carrier with OsmocomBB and stoneEDGE",
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                        "*": "[[File:Open Source GSM Phone Call.jpg|thumb|Open source GSM phone call with OsmocomBB.]]\n\n==Introduction==\nGSM is the most ubiquitous mobile communication standard\nworldwide. Millions of people use is every day. Recently, a physical\nlayer (PHY) implementation of the GSM mobile communication standard\nhas been completed at the Integrated Systems Laboratory (IIS). It\ncomprises a state of the art transceiver chip and a digital baseband\non an ASIC called [[stoneEDGE]]. In the past few years, the open source community behind\nthe OsmocomBB project [1] has implemented a relatively\ncomplete GSM protocol stack for a Mobile Station (MS). In this project\nthe student will combine the IIS PHY with OsmocomBB software to build\na complete MS capable of placing and receiving voice calls.\n\n==Project Description==\nA cellular modem consists of various portions:\n* Radio Frequency (RF) analog processing\n* Digital Baseband (DBB) processing\n* L2/L3 processing on CPU\nA functional RF/FPGA/CPU testbed for fast prototyping is available\nwhich distributes modem tasks over three separate boards. These are\n* Double RF (from project partner ACP www.newacp.ch) on IIS [[EvalEDGE]] v1.0 board\n* IIS DBB from [[stoneEDGE]] on Virtex-6 FPGA on ML605 board (see [2])\n* L2/L3 processing on ARM core on ZedBoard (see [3])\nThis IIS 2G testbed shall be used during this project.\n\nThe OsmocomBB project uses a similar modem partitioning. The PHY runs\non an old Motorola C123 where as L2/L3 runs on a regular Linux\nmachine. Communication between the PHY and L2/L3 takes place over a\nserial link using a simple protocol, called L1CTL.\n\nThe IIS 2G testbed has no operating system (OS) running on\nit. Therefore, in order to run OsmocomBB L2/L3 on the testbed a small\nOS is required. The free and real-time OS FreeRTOS [4]\nshall be used. In a first step, FreeRTOS needs to be ported onto the 2G\nIIS testbed. In a second step, OsmocomBB L2/L3 can be run on the\nFreeRTOS on the testbed to support voice calls.\n\n===Status: Completed ===\n: Student: sem15f15\n: Supervision: [[User:Weberbe|Benjamin Weber]], [[User:Kroell|Harald Kr\u00f6ll]]\n\n===Professor===\n[http://www.iis.ee.ethz.ch/people/person-detail.html?persid=78758 Qiuting Huang]\n\n==References== \n\n[1] OsmocomBB. http://bb.osmocom.org/trac/, April 2015.\n\n[2] XILINX. Virtex-6 FPGA ML605 Evaluation Kit.\nhttp://www.xilinx.com/ml605,\nApril 2015.\n\n[3] AVNET. ZedBoard.\nhttp://zedboard.org/product/zedboard,\nApril 2015.\n\n[4] FreeRTOS - Market leading RTOS (Real Time Operating System) for embedded systems\nwith Internet of Things extenstions. http://www.freertos.org/, April 2015.\n\n\n[[Category:Digital]]\n[[Category:Completed]]\n[[Category:2015]]\n[[Category:Semester Thesis]]\n[[Category:Telecommunications]]\n[[Category:Software]]\n[[Category:Weberbe]]\n[[Category:Kroell]]"
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            "894": {
                "pageid": 894,
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                "title": "Real-Time ECG Contractions Classification",
                "revisions": [
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                        "*": "[[Category:Biomedical System on Chips]]\n[[File:ecg_task.png|thumb|ECG Task]]\n\n==Introduction==\nECG (electrocardiography) is the process of recording the heart-generated electrical activity over a period of time using skin-placed electrodes.\nA well defined sequence of atrial and ventricles contractions allow the blood to be pumped in the circulatory systems, essential to enable many physiological functions as well as life. Such recorded electrical activity reflects the underlying contractions of the heart and have well defined taxonomy (according to shapes, peaks, timing, etc), allowing to detect and label heart diseases.\n\nNowadays, ECG interpretation relies on eye inspection by trained medical doctors, with expensive and time-consuming manual analysis. Patient-to-patient variability, different recording conditions and settings and patient clinical conditions makes the automation of this task very challenging. \nFor this reason the ECG monitoring is currently limited to short-time scales (minutes to hours) in well controlled environments and setups (clinics, expensive devices). \n\nEven though current ECG exams works well if there is already the suspect of a heart disease going on, this prevents to detect outliers in the ECG, i.e., abnormal contractions that rarely happens and the chances to observe them under clinical ECG are very low. \nSimilarly, drifting over time of ECG parameters may not be noticed with normal ECG acquisitions, as the time scale is very short.\nAs a result, many potential diseases can't be early-detected due to these limitations. Technology advancement in hardware design and in data-driven algorithms give a hope providing tools to change ECG detection, from expensive non-routine exams, to cheap daily monitoring.\n\n==Short Description==\n\nYour mission, should you choose to accept it, is to join our active research into biomedical system design. The approach is to used state-of-the-art machine learning algorithms to classify different ECG contractions. Particular emphasis will be devoted to detect outliers in the ECG, which usually are not even noticed by the patient, but by which are known to anticipated important disease if not timely cured [1]. Given the constrained conditions under which we operate, i.e, wearable devices, energy-efficiency is of paramount importance. \n\nThe starting point is the VivoSoC platform, a system on chip capable to acquire and process ECG signals. An open-source labelled ECG dataset is available online ready to be used [2][3].\n\nThe task includes the following main sub-points:\n<ul><li> Understand the ECG basics and interpret the dataset.</li>\n<li>  Develop (high-level Phython or Matlab) a supervised-learning classification algorithm to classify the ECG contractions. </li>\n<li>  Map the algorithm in the VivoSoC hardware (C-programming PULP).</li>\n<li>  Conduct in-vivo experiments to validate the method with a realistic setting.</li></ul>\n\nThe task is anyway flexible and it will be adapted the student skills and will.\n\n\n===Status: Completed ===\n: Semester Project in Fall 2018 (Sumu Zhao, sem18h16)\n: Supervised by: [[:User:Glaserf | Florian Glaser]], [[:User:xiaywang|Xiaying Wang]]\n<!--\n===Status: Completed ===\n: Fall Semester 2014 (sem13h2)\n: Matthias Baer, Renzo Andri\n--->\n<!--\n===Status: In Progress ===\n: Student A, StudentB\n: Supervision: [[:User:Mluisier | Mathieu Luisier]]\n--->\n\n===Character===\n: 40% Theory and Algorithms\n: 40% Implementation (C coding)\n: 20% Verification and Testing\n\n===Prerequisites===\n: Knowledge in Machine Learning, from  preprocessing, feature extraction, classifier, supervised-learning\n: Embedded system programming\n: Basic analog / ADC / sampling theory hands-on knowledge.\n: C/C++\n\n===Professor===\n: [http://www.iis.ee.ethz.ch/people/person-detail.html?persid=194234 Luca Benini]\n===References===\n: [1]  B.A.Koplanand et al, \u201cVentricular Tachycardia and Sudden Cardiac Death,\u201d Elsevier Mayo Clinic Proceedings, vol. 84, no. 3, pp. 289\u2013297, 2009.\n: [2] https://www.physionet.org/physiobank/database/mitdb/\n: [3] https://physionet.org/lightwave/\n\n[[#top|\u2191 top]]\n\n[[Category:Digital]]\n[[Category:Analog]]\n[[Category:Semester Thesis]]\n[[Category:Master Thesis]]\n[[Category:Completed]]\n[[Category:Glaserf]]\n[[Category:Xiaywang]]\n[[Category:Rovereg]]\n[[Category:PULP]]\n[[Category:Software]]\n[[Category:Processor]]\n[[Category:Biomedical System on Chips]]"
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