Teaching and Examination Regulations Fulltime Master Sensor System Engineering. Hanze University of Applied Sciences, Groningen

Transcription

1 Teaching and Examination Regulations Fulltime Master Sensor System Engineering Hanze University of Applied Sciences, Groningen Adopted by the Dean of the Institute of Engineering on 30 June 2016 These Regulations take effect from 1 September

2 Version history Revision date Summary corrections By Version First concept J. Bruining Input Programme Committee inserted J. Bruining Version adjusted for (approved by SPC and Dean) J.Bruining 1.0 2

3 Contents 1. Study Programme Examining Board and Assessment Committee Admission Committee School Participation Council of the School/Academy/Institution Programme Committee Learning Outcomes General programme learning outcomes Programme Learning outcomes for specialisation module Health: The Master s level Programme Outline Mode of Study: Full-time, Part-time, Work-based Specialisations and Differentiations Study Pathways Curriculum Overview Curriculum Admission Requirements Required Prior Learning Language Requirements for Admission to Programmes taught in English Foreign Students: Legal Residence Requirement Examinations Chronology of Examinations Number of Examination Resits (outside of written examinations) Anti-Plagiarism Rules Placements and Excursions Compulsory Attendance Foreign Languages used in the Programme Cum Laude Regulations Examination Regulations NAMES and ABBREVIATIONS

4 TEACHING REGULATIONS OF THE STUDY PROGRAMME MASTER SENSOR SYSTEM ENGINEERING 1. Study Programme This document contains the teaching and examination regulations of the Master Sensor System Engineering. This programme is offered by the Institute of Engineering, based in Assen, and is part of the wider Institute of Engineering, one of the 17 schools of the Hanze University of Applied Sciences, Groningen (Hanze UAS). The teaching and examination regulations incorporate the general examination regulations of Master programmes at the Hanze UAS (Appendix I). The teaching and examination regulations apply to all students who are enrolled in the programme. Programme Description Graduates from the Master Sensor System Engineering are professional Engineers capable of conceiving, designing and managing end-to-end products and user level services in technical environments where the generation, management, analysis and application of (potentially large) data streams from sensors play a central role. They have the professional skills to carry projects beyond the proof-of-concept phase into prototypes and user applications. They can advise clients on conceptual solutions and on optimal ways to analyse complex systems and data flows. Their job-title will vary depending on the domain and specialisation, but has the common denominator of Sensor System Architect. Educational principles The educational basis of the Master Sensor System Engineering is provided by developments in the professional practice of Sensor Technology. Graduates have advanced technical knowledge of sensor technology with systems overview and a problem-oriented approach that allows them to take a user/service perspective. They have competences to design architectures for big-data sensor systems and data-centric sensor applications, including the modelling of complex data flows and analysis algorithms. They are aware of real-world limitations and constraints, both physical, societal and regulatory. They have the professional skills to work in intercultural and multidisciplinary teams, to excel in interaction with customers, colleagues and partners in the value chain. The programme s main educational characteristics are: Competence based learning with focus on academic, technical and social and communicative learning outcomes. Integrated learning of knowledge skills. Development of professional and personal competences Studying in an international environment 1.1 EXAMINING BOARD AND ASSESSMENT COMMITTEE The Examining Board is responsible for assuring the quality of the programme by supervising the content, method and level of the examinations. It has the duty to determine whether graduates have achieved the learning outcomes described in the TER (Teaching and Examination Regulations). The members of the Examining Board are appointed by the Dean. The Assessment Committee is responsible for monitoring the quality of examinations and operates under the supervision of the Examining Board. 4

5 The members of the Examining Board and the Assessment committee and how to contact them will be made available on MijnHanze.nl 1.2 ADMISSION COMMITTEE The Admission Committee advises the Dean about the admission of students. The members of the committee are appointed by the Dean. The members of the admission committee and how to contact them will be made available on MijnHanze.nl 1.3 SCHOOL PARTICIPATION COUNCIL OF THE SCHOOL/ACADEMY/INSTITUTION The representative council of a school, academy or institution is a democratically elected body. One half of the council is comprised of students and the other half of university staff. The members of the school participation council and how to contact them will be made available on MijnHanze.nl 1.4 PROGRAMME COMMITTEE The Programme Committee gives advice about matters relating to teaching in the school, academy or institution. Half of its members are students. All the members are appointed by the Dean. The members of the programme committee and how to contact them will be made available on MijnHanze.nl 2. Learning Outcomes The following programme learning outcomes have been defined for graduates of the Master Sensor System Engineering. These programme learning outcomes are thought to be essential for the future Sensor Technologist. These key competences agree with the Dublin Descriptors for a Master level programme (see below) and implement the EUR-ACE Programme Outcomes for a Professional Master of Engineering. These learning outcomes comprise: 2.1 GENERAL PROGRAMME LEARNING OUTCOMES M1. Modelling Meaningful Data: The graduate creates models for advanced sensor systems that transform raw sensor data into meaningful data for a client or an automated system by applying complex analysis methods, taking into account state-of-the-art technologies and using model based reasoning from an multi-disciplinary perspective. M2. Building Intelligent Architectures: The graduate independently designs the architecture of advanced sensor systems that process big data and use high performance processing. Within the architecture the graduate is able to make critical decisions on the location of system intelligence, by taking into account technical and financial specifications, as well as ethical and environmental considerations. 5

6 M3. Creating Reliable Services: The graduate advises on or creates services for non-specialised clients that provide reliable decisions based on complex data from both advanced sensor systems and contextual information, using the data and data flow in a responsible way. To this end, the graduate takes the lead in gathering requirements and boundary conditions from the full range of stakeholders, in a potentially international environment. M4. Designing Towards Prototype: The graduate extends upon a validated proof-of-concept to improve the robustness, reliability and usability of an advanced sensor system by user acceptance testing and/or system level field testing while utilising experts in the field of application when necessary. In doing so, the graduate assesses the effects of the system on the environment, as well as the acceptance factor and societal impact. M5. Professional Skills: The graduate performs as a leader in a team that may be composed of different disciplines and nationalities by independently creating a network of stakeholders that can help in solving a problem in an interdisciplinary setting. The graduate acts as a proactive sparring partner for different stakeholders in the project by advising, asked and non-asked for, clients, on conceptual solutions and optimal ways to analyse complex systems and data flows, by judging end-user implications, using creativity and self reflection skills. M6. Being Aware of Impact: The graduate optimizes the advice or design of an advanced sensor application through critical reflection on the impact on society and environment, based on ethical aspects, cradle-to-cradle, risk analysis, green engineering and unexpected information. In this process, the graduate proactively organises stakeholder groups and assessment meetings. M7. Performing Responsible Research: The graduate independently gathers, selects and analyses relevant (new) information in an responsible way, formulating and critically verifying hypotheses through analysis or experiments, in order to validate and develop advanced sensor systems in unfamiliar contexts. By evaluation of the outcome of the experiments, the graduate contributes to the state of the art in the field of advanced sensor technology and finds original solutions to existing problems. M8. Contributing to Innovation: The graduate formulates new business cases by proactively collecting business data. The graduate identifies any intellectual property following from research and development activities. The graduate gains knowledge by continually critically judging insights originating from the forefront of advanced sensor technology and being aware of priorities of stakeholders. 2.2 PROGRAMME LEARNING OUTCOMES FOR SPECIALISATION MODULE HEALTH: H1. Applying Sensor Technology in Health: The graduate applies the latest developments in advanced sensor technology and uses interdisciplinary knowledge in order to solve an unfamiliar, health-related problem in society by careful consideration of ethical implications. 6

7 H2. Developing Health Applications: The graduate develops advanced sensor systems for health applications compliant with applicable regulations, health standards and financial models. 2.3 THE MASTER S LEVEL The Master s level is characterised by the student s expertise in their specialism. Students are (semi)autonomous, demonstrating independence in the negotiation of assessment tasks (including the Master thesis project) and the ability to evaluate, challenge, modify and develop theory and practice. Students are expected to demonstrate an ability to isolate and focus on the significant features of problems and to offer synthetic and coherent solutions, contribute to generizable knowledge, with some students producing original or innovative work in their specialism that is worthy of publication or public performance or display. From the point of view of the framework for Qualifications of the European Higher Education Area, the Master Sensor System Engineering is a second cycle programme. This means it should develop programme learning outcomes in line with Dublin Descriptors for the Master level. The table below describes the alignment of the Master Sensor System Engineering programme learning outcomes with these descriptors. The table below summarizes the qualifications that students need to comply with for being awarded Master level ( second cycle ). Qualifications that signify completion of the second cycle (= Master level) are awarded to students who: 1. Knowledge and understanding have demonstrated knowledge and understanding that is founded upon and extends and/or enhances that typically associated with the first cycle, and that provides a basis or opportunity for originality in developing and/or applying 2. Applying knowledge and understanding ideas, often within a research context. can apply their knowledge and understanding, and problem solving abilities in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their field of study. 3. Making judgments have the ability to integrate knowledge and handle complexity, and formulate judgements with incomplete or limited information, but that include reflecting on social and ethical responsibilities linked to the application of their knowledge and judgements. 4. Communication can communicate their conclusions, and the knowledge and rationale underpinning these, to specialist and non specialist audiences clearly and unambiguously. 5. Learning skills have the learning skills to allow them to continue to study in a manner that may be largely self-directed or autonomous. 7

8 The table below shows how the Dublin descriptors are covered by the ProgrammeLearning Outcomes defined for the Master. Dublin descriptors 1. Knowledge and understanding 2. Applying knowledge and understanding Professional competencies Master Sensor System Engineering M1. Modelling Meaningful Data X X * M2. Building Intelligent Architectures X * X * M3. Creating Reliable Services X * X X * M4. Designing Towards Prototype * * X X M5. Professional Skills * * * X X M6. Being Aware of Impact * X X X M7. Performing Responsible Research X * * X X M8. Contributing to Innovation * X * * * Professional competencies Master Sensor System Engineering, Specialisation Health H1. Applying Sensor Technology in Health * X X * X H2. Developing Health Applications * * * * 3. Making judgments 4. Communication 5. Learning skills X = high contribution * = contribution The EUR-ACE Programme Outcomes defines six programme outcomes for a Professional Master of Engineering. 1. Knowledge and understanding 2. Engineering analysis 3. Engineering design 4. Investigations 5. Engineering practice 6. Transferable skills 8

9 These programme outcomes are summarised below with a description how they are implemented in the Master Sensor System Engineering. 1. Knowledge and Understanding The underpinning knowledge and understanding of science, mathematics and engineering fundamentals are essential to satisfying the other programme outcomes. Graduates should demonstrate their knowledge and understanding of their engineering specialisation, and also of the wider context of engineering. Second Cycle graduates should have: an in-depth knowledge and understanding of the principles of their branch of engineering a critical awareness of the forefront of their branch In-depth engineering knowledge and understanding is covered in the foundational programme learning outcomes: Modelling Meaningful Data, Building Intelligent Architectures, Creating Reliable Services and Designing Towards Prototype. As the titles imply, these programme learning outcomes go significantly beyond the average level, bringing students up to the front, and teaching them to assess critically the global technology base. 2. Engineering Analysis Graduates should be able to solve engineering problems consistent with their level of knowledge and understanding, and which may involve considerations from outside their field of specialisation. Analysis can include the identification of the problem, clarification of the specification, consideration of possible methods of solution, selection of the most appropriate method, and correct implementation. Graduates should be able to use a variety of methods, including mathematical analysis, computational modelling, or practical experiments, and should be able to recognise the importance of societal, health and safety, environmental and commercial constraints. Second Cycle graduates should have: the ability to solve problems that are unfamiliar, incompletely defined, and have competing specifications the ability to formulate and solve problems in new and emerging areas of their specialisation the ability to use their knowledge and understanding to conceptualise engineering models, systems and processes the ability to apply innovative methods in problem solving Conceptualisation of models is an essential prerequisite for Modelling Meaningful Data and Creating Reliable Services. Engineering processes (including user specifications) are covered in depth in Designing Towards Prototype. Dealing with incomplete information is covered in all programme learning outcomes, but a formal process is covered in Performing Responsible Research. The field of Sensor System Engineering requires students to develop an acurate awareness of new and emerging technologies, in particular for the specialisation module. 3. Engineering Design Graduates should be able to realise engineering designs consistent with their level of knowledge and understanding, working in cooperation with engineers and non-engineers. The designs may be of devices, processes, methods or artefacts, and the specifications could be wider than technical, including an awareness of societal, health and safety, environmental and commercial considerations. Second Cycle graduates should have: an ability to use their knowledge and understanding to design solutions to unfamiliar problems, possibly involving other disciplines an ability to use creativity to develop new and original ideas and methods an ability to use their engineering judgement to work with complexity, technical uncertainty and incomplete information Modelling Meaningful Data, Creating Reliable Services, Designing Towards Prototype and Contributing to Innovation address complex systems, requiring creativity in combination with engineering rigour. In both cases the bachelor level is exceeded by the introduction of modelling, client-interaction and field-testing, adding to complexity and 9

10 uncertainty both in specifications, but also in verification. 4. Investigations Graduates should be able to use appropriate methods to pursue research or other detailed investigations of technical issues consistent with their level of knowledge and understanding. Investigations may involve literature searches, the design and execution of experiments, the interpretation of data, and computer simulation. They may require that data bases, codes of practice and safety regulations are consulted. Second Cycle graduates should have: the ability to identify, locate and obtain required data the ability to design and conduct analytic, modelling and experimental investigations the ability to critically evaluate data and draw conclusions the ability to investigate the application of new and emerging technologies in their branch of engineering The handling and application of information in an international context is most directly addressed in Performing Responsible Research and Contributing to Innovation. Aspects of modelling and evaluation of emerging technologies are of course also covered in M1-M4, as well as in H1-H2. 5. Engineering Practice Graduates should be able to apply their knowledge and understanding to developing practical skills for solving problems, conducting investigations, and designing engineering devices and processes. These skills may include the knowledge, use and limitations of materials, computer modelling, engineering processes, equipment, workshop practice, and technical literature and information sources. They should also recognise the wider, non-technical implications of engineering practice, ethical, environmental, commercial and industrial. Second Cycle graduates should have: the ability to integrate knowledge from different branches, and handle complexity a comprehensive understanding of applicable techniques and methods, and of their limitations a knowledge of the non-technical implications of engineering practice Non-technical implications are covered in Being Aware of Impact, inter-disciplinary research is an essential part of Performing Responsible Research, Professional Skills and Applying Sensor Technology in Health. The learning outcome Modelling Meaningful Data gives students the tools to assess the limitations of technology. Handling complex systems interdisciplinary is key to M2-M4 and H1-H2. 6. Transferable Skills The skills necessary for the practice of engineering, and which are applicable more widely, should be developed within the programme. Second Cycle graduates should be able to: fulfil all the Transferable Skill requirements of a First Cycle graduate at the more demanding level of Second Cycle function effectively as leader of a team that may be composed of different disciplines and levels work and communicate effectively in national and international contexts Technical, research and professional skills are deepened with respect to the Bachelor level explicitly in Professional Skills and Applying Sensor Technology in Health. They are also covered by M1-M4 and H2, where an international context is implied throughout. 10

11 3. Programme Outline 3.1 MODE OF STUDY: FULLTIME, PARTTIME, WORKBASED The Master Sensor System Engineering is a fulltime programme. 3.2 SPECIALISATIONS AND DIFFERENTIATIONS The Master Sensor System Engineering has only one specialisation, namely Health. 3.3 STUDY PATHWAYS The Master Sensor System Engineering has only one study pathway. 3.4 CURRICULUM OVERVIEW Master Semester 1 OC EC 1.1 Linear Algebra SEVM4LAL Modelling and Simulation SEVM4MS Advanced Data Analysis SEVM4ADA Data Centric Architectures SEVM4DCA Products and Services in Health SEVM4PSH Sensor Applications in Health SEVM4SAH Progress Test 1 SEVM16PT Progress Test 2 SEVM16PT Professional Skills 1 SEVM4PFS Research and Ethics Community Contribution 1 SEVM4RET1 SEVM4CC1 Total Master Semester 2 Master Thesis 2.1 Master Thesis SEVM4MT Professional Skills 2 SEVM4PFS Research & Ethics 2 SEVM4RET Progress Test 3 SEVM16PT3 2 OC EC Total 35 11

12 4. Curriculum Heading Description Title Linear Algebra English Title Linear Algebra Code SEVM4LAL Academic Year 2016/2017 Workload 4 ECTS Competencies M1 Modelling Meaningful data M2 Building Intelligent Architectures Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Mathematics 2 of Bachelor Advanced Sensor Applications Statistics 2 of Bachelor Advanced Sensor Applications Level O Introductory X Deepening O Advanced Content The course covers the following linear algebra topics: linear systems, matrix algebra, determinants, vector spaces, Eigenvalues, Eigenvectors, orthogonality, least squares, symmetric matrices and quadratic forms. Next to this it sketches some applications in the field of engineering. The theoretical part of this module is assessed during the progress test. At the end of this module the student is able to: - The student can manipulate matrices to solve a linear system of equations. - The student can analyse a dynamical system using determinants to find eigenvalues and eigenvectors. - The student can solve least squares problems. - The student can apply singular value decomposition to problems in data analysis and signal Processing. Teaching method Action Learning x Practical/training Thesis Problem-Based Learning (PBL) International thesis Project learning Guest lecture International placement x Lecture Placement/work-based learning (WBL) Individual tutoring Supervised learning Peer coaching x Tutorial Assessment Attendance Professionalism Oral examination Professional product x Assignment x Written examination Other Skills assessment Portfolio assessment Report Presentation Work discussion Costs - Study materials Title Author ISBN Compulsory/recommended Comment Linear Algebra and Its Applications, 4 th Edition Introduction to Linear Algebra, 4 th edition David Lay Gilbert Strang Compulsory Recommended Accompanying video lectures: Language of O Dutch x English O German instruction Particulars - Contact Corina Vogt, c.b.vogt@pl.hanze.nl, telephone: , location: Assen, room: Curriculum year x 1 Period x Year/Period in the curriculum x 1.1 O 1.2 O 1.3 O

13 Heading Description Title Modelling and Simulation English Title Modelling and Simulation Code SEVM4MS Academic Year 2016/2017 Workload 4 ECTS Competencies M1 Modelling Meaningful Data Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Project theme 5 of Bachelor Advanced Sensor Applications Digital Signal Processing of Bachelor Advanced Sensor Applications Data Analysis of Bachelor Advanced Sensor Applications Intelligent Sensors of Bachelor Advanced Sensor Applications Sensor Data of Bachelor Advanced Sensor Applications Level O Introductory O Deepening X Advanced Content The course covers the following topics: stationary discrete-time stochastical processes and models, linear and non-linear dynamical system models, (normalized) Least-Mean-Square adaptive filters, Recursive Least Squares Adaptive Filters and Kalman filtering, model simulation tools, model-based reasoning. The theoretical part of this module is assessed during the progress test. At the end of this module the student is able to: - Model and simulate (sensor) systems using tools such as Modelica and pysimulator. - Describe systems in the time-domain and/or frequency domain using stationary discrete-time stochastical processes and models, such as autoregressive and moving-average models. - Describe systems using linear and nonlinear dynamical system models by applying techniques such as state space, phase portrait, linearization and bifurcations. - Clean data using advanced filter techniques, such as (normalized) Least-Mean-Square adaptive filters, Recursive Least Squares Adaptive Filters and Kalman filtering. - Analyse sensor systems using model-based reasoning - Represent data in a meaningful way. Teaching method Action Learning Practical/training Thesis Problem-Based Learning (PBL) International thesis Project learning Guest lecture International placement x Lecture Placement/work-based learning (WBL) Individual tutoring Supervised learning Peer coaching x Tutorial Assessment Attendance Professionalism Oral examination Professional product x Assignment Written examination Other Skills assessment Portfolio assessment Report Presentation Work discussion Costs - Study materials Title Author ISBN Compulsory/recommended Comment Adaptive Filter Theory, 5 th edition S. Haykin Compulsory International Edition Language of O Dutch x English O German instruction Particulars - Contact Dr. Berend-Jan van der Zwaag, b.j.van.der.zwaag@pl.hanze.nl Telephone: Curriculum year x 1 Period x Year/Period in the O 1.1 x 1.2 O 1.3 O 1.4 curriculum 13

14 Heading Description Title Advanced Data Analysis English Title Advanced Data Analysis Code SEVM4ADA Academic Year 2016/2017 Workload 4 ECTS Competencies M1 Modelling Meaningful Data M2 Building Intelligent Architectures Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Statistics 1 of Bachelor Advanced Sensor Applications Digital Signal Processing of Bachelor Advanced Sensor Applications Statistics 2 of Bachelor Advanced Sensor Applications Intelligent Sensors of Bachelor Advanced Sensor Applications Sensor Data of Bachelor Advanced Sensor Applications Programming Java 2 of Bachelor Advanced Sensor Applications or similar experience with an OO language Level O Introductory O Deepening X Advanced Content The course covers the following topics: computer algebra systems (Python based), sensor fusion architectures, common representational format, temporal and spatial alignment, error propagation analysis, principal component analysis, independent component analysis, support vector machines, k-means clustering. The theoretical part of this module is assessed during the progress test. At the end of this module the student is able to: - The student can manipulate numerical data using Python with the linear algebra package Numpy and the plotting package MatPlotlib. - The student can analyse system requirements to identify applicable sensor fusion architectures. - The student can analyse data acquisition by a sensor system to construct a common representational format. - The student can adapt sensor system design to create correct temporal and spatial alignment of multi-sensor data. - The student can create a sensor system which combines multi-sensor data into correct interpretations. - The student can analyse a data analysis task and compare features to evaluate their fitness for the task at hand. - The student can evaluate whether a data analysis task requires dimensionality reduction, blind source separation, cluster analysis or classification. - The student can interpret high dimensional data using dimensionality reduction techniques such as principal component analysis. - The student can interpret multi-sensor data: o using blind source separation techniques like independent component analysis. o using cluster analysis techniques. o using classification algorithms like support vector machines. Teaching method Action Learning Practical/training Thesis Problem-Based Learning (PBL) International thesis x Project learning Guest lecture International placement x Lecture Placement/work-based learning (WBL) Individual tutoring Supervised learning Peer coaching x Tutorial Assessment Attendance Professionalism Oral examination Professional product Assignment Written examination Other Skills assessment x Portfolio assessment Report Presentation Work discussion Costs - Study materials 14

15 Title Author ISBN Compulsory/re Comment commended Multi-Sensor Data Fusion: An Introduction, Part I: Basics and Part II: Representation H.B. Mitchell Recommended Machine Learning: Peter A. Flach Compulsory Students need to buy this book. The Art and Science of Algorithms that Make Sense of Data An Introduction to I. Gyuon and A. Journal of Machine Compulsory Article: available electronically from Hanze Variable and Feature Selection Elisseeff Learning Research 3, (2003) p mediatheek. Neural Networks, Vol. 13, No 4, pages An Efficient k- Means Clustering Algorithm: Analysis and Implementation A Tutorial on Principal Component Analysis SVMs a practical consequence of learning theory Lectures on python and numpy from version 4 of the software carpentry A. Hyvärinen and E. Oja T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y. Wu IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, 2002 Compulsory Compulsory Article: made available by authors through 99_tutorialweb Article: available electronically from Hanze mediatheek. Jonathon Shlens Compulsory Article: available from the author website through Bernhard Scholkopf IEEE Intelligent Systems, pages 18-21, July/Aug 1998 Compulsory Compulsory Article: available from the author website /details/bs Matplotlib gallery Compulsory Multi-Sensor Fusion Ghuang-Zhong Yang and Xiaopeng Recommended Surface-Based and Probabilistic Atlases of Primate Cerebral Cortex Support Vector Machines and other kernel-based learning methods Language of Hu David C. Van Essen and Donna L. Dierker John Shawe-Taylor &NelloCristianini (2007), Neuron Recommended Cambridge University Press, 2000 O Dutch x English O German Recommended instruction Particulars - Contact Ronald van Elburg, R.A.J.van.Elburg@pl.hanze.nl, telephone: , location: Assen, room: 1.06 Curriculum year x 1 Period x Year/Period in the curriculum x 1.1 O 1.2 O 1.3 O

16 Heading Description Title Data Centric Architectures English Title Data Centric Architectures Code SEVM4DCA Academic Year 2016/2017 Workload 4 ECTS Competencies M2 Building Intelligent Architectures M5 Professional Skills M6 Being Aware of Impact Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Sensor Networks 1 of Bachelor Advanced Sensor Applications Intelligent Sensors of Bachelor Advanced Sensor Applications Level O Introductory O Deepening X Advanced Content In this module students are trained in architectural design at two conceptual levels. At the level of System Architectures for big data applications they learn about top-level trade-offs, e.g. between measured data rates and required processing power. They get introduced to high-performance computing and streaming database technology. At the level of Digital Signal Processing Architectures, they learn key concepts (fixed point, floating point) and technologies (FPGA, GPU, Mixed signal chips). Linear and Functional programming as well as Parallel processing are covered. The theoretical part of this module is assessed during the progress test At the end of this module the student is able to: - Design and/or implement a Digital Signal Processing Architecture based on fixed point core, floating point core, PLD, FPGA, GPU or mixed signal systems - Implement algorithms by means of sequential (i.e. linear) programming. - implement algorithms by means of parallel processing - Design and implement a system architecture using streaming database technology - Design and implement an architecture for a given sensor system algorithm capable of big data streams and high performance processing - Design based on top-down approach an appropriate architecture for an advanced sensor system Teaching method Action Learning x Practical/training Thesis Problem-Based Learning (PBL) International thesis Project learning Guest lecture International placement x Lecture Placement/work-based learning (WBL) Individual tutoring Supervised learning Peer coaching x Tutorial Assessment Attendance Professionalism Oral examination Professional product x Assignment Written examination Other Skills assessment Portfolio assessment Report Presentation Work discussion Costs - Study materials Title Author ISBN Compulsory/recommended Comment Embedded Systems Architecture, Second Edition: A Comprehensive Guide for Engineers and Programmers Tammy Noergaard Compulsory Software Mark W. Maier, David IEEE Computing, April Compulsory Downloadable article 16

17 Architecture: Introducing IEEE Standard 1471 From Microprocessors to Nanostores: Rethinking Data- Centric Systems Gordon: An Improved Architecture for Data-Intensive Applications Language of Emery, Rich Hilliard 2001, pp Parthasarathy Ranganathan Adrian M. Caulfield, Laura M. Grupp& Steven Swanson IEEE Computer, January 2011 (vol. 44 no. 1), pp O Dutch x English O German IEEE Micro, January/February 2010 (vol. 30 no. 1), pp Compulsory Compulsory instruction Particulars - Contact Ir. J.W. Knobbe PDEng., j.w.knobbe@pl.hanze.nl, Telephone: Curriculum year x 1 Period x Year/Period in the curriculum x 1.1 O 1.2 O 1.3 O 1.4 Downloadable article Downloadable article Heading Description Title Products and Services in Health English Title Products and Services in Health Code SEVM4PSH Academic Year 2016/2017 Workload 4 ECTS Competencies M2 Building Intelligent Architectures M4 Designing Towards Prototype M5 Professional Skills M8 Contributing to Innovation H1 Applying Sensor Technology in Health H2 Developing Health Applications Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Part of Reliability Engineering 1 and 2 (technology assessment, CPK/PPK, Gage R&R, Design of Experiments, Tolerance Design, Validation) of Bachelor Advanced Sensor Applications Methodical Design of Bachelor Advanced Sensor Applications Level O Introductory O Deepening X Advanced Content A majority of the course material covers the following 5 areas of designing medical products and services: - Case analysis: Formulating business cases in the health domain; problem definitions, design goals and subgoals and identify stakeholders. - Business case formulation: Investigate and assess possible and existing business models, stakeholders and associated regulations and systems. - Requirements engineering: Translating business cases, user cases and user requirements into product/service requirements and technical specifications. - Ideation for innovation: generating ideas and applying advanced technologies and algorithms. Looking for state of the art, but out of context applications. - System architecture: distributing functionality over (sensor)hardware, transport, service software etc, taking into account the full lifecycle. 17

18 The theoretical part of this module is assessed during the progress test. At the end of this module the student is able to: - Methodically and systematicaly analyse a problem case in the health domain and present a convincing analysis of the problem, possible goals and current solutions and their short comings in multiple presentations and a report. - Methodically design a product, from case to concept, at top-level system architecture, including use scenarios, visualisations and specification and present it as a business case in both a report and presentation. - Understand product development in the health domain; business models, the regulatory landscape and stakeholders. - Present and communicate, purposeful and convincingly, ideas, business cases, needs and questions for co-operation. Teaching method Action Learning x Practical/training Thesis x Problem-Based Learning (PBL) International thesis x Project learning x Guest lecture International placement x Lecture Placement/work-based learning (WBL) Individual tutoring x Supervised learning x Peer coaching Tutorial Assessment x active participation (1 ECTS) Professionalism Oral examination Professional product Assignment Written examination x Progress Test (1 ECTS) Skills assessment Portfolio assessment x Report (2 ECTS) x Presentation (1 ECTS) Work discussion Costs - Study materials Title Common mistakes in medical device development Requirements What s the Big Deal? When Should You Start Managing Requirements? Using V Models for Testing Author ISBN Compulsory/recommended Comment Anney Majewski Infographic, August 2 nd, 2015 Lou Wheatcraft - (blog post 13 August 2012) Lou Wheatcraft - (blog post 27 September 2012) Donald Firesmith - (SEI blog post 11 November 2013) O Dutch x English O German Compulsory Compulsory Compulsory Compulsory Language of instruction Particulars - Contact Ward van der Houwen vdhouwen@gmail.com Curriculum year x 1 Period 1 x Year/Period in the curriculum O 1.1 x 1.2 O 1.3 O 1.4 namsa.com/infographiccommon-mistakes-in-themedical-device-developmentcontinuum-2/ reqexperts.com/blog/2012/08/req uirements-importance reqexperts.com/blog/2013/09/wh en-should-you-start-managingrequirements blog.sei.cmu.edu/post.cfm/using -v-models-testing

19 Heading Description Title Sensor Applications in Health English Title Sensor Applications in Health Code SEVM4SAH Academic Year 2016/2017 Workload 3 ECTS Competencies M6 Being Aware of Impact H1 Applying Sensor Technology in Health H2 Developing Health Applications Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Biology 1 of Bachelor Advanced Sensor Applications Biochemistry of Bachelor Advanced Sensor Applications Biology 3 of Bachelor Advanced Sensor Applications Sensor Data of Bachelor Advanced Sensor Applications Level O Introductory x Deepening O Advanced Content During this module a number of topics related to sensor applications in health will be covered: - Human physiology - Principles and applications of biochemical sensor technology. - Diagnostics - Hospital and home care and monitoring - The theoretical part of this module is assessed during the progress test. At the end of this module the student is able to: - - Explain the physical, chemical or biological principles of a given sensor that is able to detect a specific biological signal - Based on a current scientific literature choose the best type of sensor suitable for detection and analysis of a given biological signal and justify their choice - Describe basic human physiological (and pathological) functions and systems like circulation, respiration, digestion, excretion, reproduction, metabolism, immunity, locomotion, control systems (endocrine and nervous) and ageing. - Measure given basic human physiological functions using sensors and interpret the results of the measurements. - Design innovative solutions for home care monitoring taking into account ethical issues Teaching method Action Learning x Practical/training Thesis Problem-Based Learning (PBL) International thesis Project learning x Guest lecture International placement x Lecture Placement/work-based learning (WBL) Individual tutoring Supervised learning Peer coaching x Tutorial Assessment x Attendance - 0% Professionalism Oral examination Professional product x Assignment - 17% Written examination Other Skills assessment Portfolio assessment x Report - 66% x Presentation 17% Work discussion Costs - Study materials Title Author ISBN Compulsory/recommended Comment Roos and Wilson Waugh and Grant Compulsory Including on-line access Anatomy and Physiology in Health and Illness Handbook of J. Fraden Compulsory Available online 19

20 Modern Sensors Textbook of Medical Physiology, 12 th edition Guyton and Hall Recommended Yang Recommended Body Sensor Networks Healthcare Sensors Networks D.T. Huei Lai, R. Begg, M. Palaniswami Recommended Language of O Dutch x English O German instruction Particulars - Contact Dr. M. A. Kozielska-Reid, m.a.kozielska-reid@pl.hanze.nl, Phone: Curriculum year x 1 Period 1 x Year/Period in the O 1.1 x 1.2 O 1.3 O 1.4 curriculum through HanzeMediatheek Heading Description Title Progress Test 1 English Title Progress Test 1 Code SEVM4PT1 Academic Year 2016/2017 Workload 2 ECTS Competencies M1 Modelling Meaningful Data M2 Building Intelligent Architectures M3 Creating Reliable Services M4 Designing Towards Prototype M5 Professional Skills M6 Being Aware of Impact M7 Performing Responsible Research M8 Contributing to Innovation H1 Applying Sensor Technology to Health H2 Developing Health Applications Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Linear Algebra of Master Sensor System Engineering Modelling and Simulation of Master Sensor System Engineering Advanced Data Analysis of Master Sensor System Engineering Data Centric Architectures of Master Sensor System Engineering Products and Services in Health of Master Sensor System Engineering Sensor Applications in Health of Master Sensor System Engineering Community Contribution of Master Sensor System Engineering Level O Introductory O Deepening X Advanced Content Throughout the year, the student is tested at least 3 times on the theoretical knowledge of all the modules of the Master Sensor System Engineering. Each progress test takes 3 hours. The first test takes place 10 weeks after the start of the academic year. If a student has at least 30% of answers correct, this will earn him the 2 ECTS for progress test 1.. The second test takes place 20 weeks after the start of the academic year. If a student has at least 60% of correct answers on this test, he/she will earn the 4 ECTS for progress test 2 (and the 2 ECTS for progress test 1, if the first test was failed). The test will consist of the topics covered by the following study units: 1 part Linear Algebra 1 part Modelling and Simulation 1 part Advanced Data Analysis 20

21 1 part Data Centric Architectures 1 part Products and Services in Health 2 parts Sensor Applications in Health 1 part Community Contribution Teaching method Action Learning Practical/training Thesis Problem-Based Learning (PBL) International thesis Project learning Guest lecture International placement Lecture Placement/work-based learning (WBL) Individual tutoring Supervised learning Peer coaching Tutorial Assessment Attendance Professionalism Oral examination Professional product Assignment x Written examination Other Skills assessment Portfolio assessment Report Presentation Work discussion Costs - Study materials Title Author ISBN Compulsory/recommended Comment - Language of instruction O Dutch x English O German Particulars The theoretical knowledge of all the modules of the Master is tested. Contact Dr. Esther Vertelman, e.j.m.vertelman@pl.hanze.nl, Curriculum year x 1 Period x Year/Period in the curriculum x Heading Description Title Progress Test 2 English Title Progress Test 2 Code SEVM4PT2 Academic Year 2016/2017 Workload 4 ECTS Competencies M1 Modelling Meaningful Data M2 Building Intelligent Architectures M3 Creating Reliable Services M4 Designing Towards Prototype M5 Professional Skills M6 Being Aware of Impact M7 Performing Responsible Research M8 Contributing to Innovation H1 Applying Sensor Technology to Health H2 Developing Health Applications Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Linear Algebra of Master Sensor System Engineering Modelling and Simulation of Master Sensor System Engineering Advanced Data Analysis of Master Sensor System Engineering Data Centric Architectures of Master Sensor System Engineering Products and Services in Health of Master Sensor System Engineering Sensor Applications in Health of Master Sensor System Engineering Community Contribution of Master Sensor System Engineering Level O Introductory O Deepening 21

22 Content X Advanced Throughout the year, the student is tested at least 3 times on the theoretical knowledge of all the modules of the Master Sensor System Engineering. Each progress test takes 3 hours. In order to gain the credits for this particular study unit, the student needs to answer at least 60% correct on the progress test that takes place 20 weeks after the start of the academic year, this will also give a pass for the study unit Progress Test 1. If a student fails progress test 2, 60% of correct answers on progress test 3 will be a pass for progress test 2 as well. The test will consist of the topics covered by the following study units: 1 part Linear Algebra 1 part Modelling and Simulation 1 part Advanced Data Analysis 1 part Data Centric Architectures 1 part Products and Services in Health 2 parts Sensor Applications in Health 1 part Community Contribution Teaching method Action Learning Practical/training Thesis Problem-Based Learning (PBL) International thesis Project learning Guest lecture International placement Lecture Placement/work-based learning (WBL) Individual tutoring Supervised learning Peer coaching Tutorial Assessment Attendance Professionalism Oral examination Professional product Assignment x Written examination Other Skills assessment Portfolio assessment Report Presentation Work discussion Costs - Study materials Title Author ISBN Compulsory/recommended Comment Language of O Dutch x English O German instruction Particulars The theoretical knowledge of all the modules of the Master is tested. Contact Dr. Esther Vertelman, e.j.m.vertelman@pl.hanze.nl, Curriculum year x 1 Period 1 x Year/Period in the 1.1 x curriculum Heading Description Title Professional Skills 1 English Title Professional Skills 1 Code SEVM4PFS1 Academic Year 2016/2017 Workload 2 ECTS Competencies M3 Creating Reliable Services M5 Professional Skills Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites Professional Skills courses from premaster SSE Level O Introductory O Deepening x Advanced Content The topics of the Professional Skills module will be: 22

23 - Negotiation skills You will learn about the different types of negotiation styles you can use and practice developing effective arguments. We will also see how questions should and should not be used in a negotiation context and the type of conventions that apply in intercultural negotiations. - Developing your career You will learn to use the main sources of information in your professional field. You will access scientific journals and professional magazines to figure out where the field is heading so you can stay one step ahead in your search for job, research and funding opportunities. In addition, you will learn basic skills to facilitate job-hunting after your studies. At the end of this module the student is able to: - Identify the differences between distributive and integrative bargaining and identify them in an applied setting. - Explain the main issues that affect intercultural negotiations. - Plan the way to reach goals for future career by reflecting and adjusting actions where necessary. - Identify the main steps and actions required to become a consultant engineer. Teaching method Action Learning Practical/training Thesis Problem-Based Learning (PBL) International thesis Project learning Guest lecture International placement x Lecture Placement/work-based learning (WBL) Individual tutoring Supervised learning Peer coaching x Tutorial Assessment Attendance Professionalism Oral examination Professional product x Assignment 50% Written examination Other Skills assessment Portfolio assessment Report x Presentation 50% Work discussion Costs - Study materials Title Author ISBN Compulsory/recommended Comment The engineer s career guide Hoschette, J. A Recommended Language of O Dutch x English O German instruction Particulars - Contact Dr. F. J. Guzmán Muñoz, f.j.guzman.munoz@pl.hanze.nl Curriculum year x 1 Period x 1 x Year/Period in the x 1.1 x 1.2 O 1.3 O 1.4 curriculum Heading Description Title Research and Ethics 1 English Title Research and Ethics 1 Code SEVM4RET1 Academic Year 2016/2017 Workload 3 ECTS Competencies M5 Professional Skills M6 Being Aware of Impact M7 Performing Responsible Research M8 Contributing to Innovation Target group/type Compulsory for students taking the study programme/major: Master Sensor System Engineering of course Prerequisites - Project theme 5 of Bachelor Advanced Sensor Applications 23