Sensor Technologies and Biological Sensing 2024 Lecture 01 PDF

INTRODUCTION, MEASUREMENT, SENSORS AND SENSOR CHARACTERISTICS Lecture 1 2024. 09. 11. Dr. Sándor Földi Sensor technologies and biological sensing  CONTACT AND LECTURE INFORMATION Lecturer Dr. Sándor Földi...

INTRODUCTION, MEASUREMENT, SENSORS AND SENSOR CHARACTERISTICS Lecture 1 2024. 09. 11. Dr. Sándor Földi Sensor technologies and biological sensing  CONTACT AND LECTURE INFORMATION Lecturer Dr. Sándor Földi E-mail: [email protected] or [email protected] Room: 339 Sensory robotics lab Reception hour: every Tuesday from 13:00 to 14:00 Lecture information is available on Moodle Attendance is mandatory 80% attendance of the lectures is required (this might be checked by roll call). Exam: There will be oral exams Exam includes the entire course material. 3  COURSE REQUIREMENTS Requirement of the signature: At least 80% attendance of the lectures (8 out of 11) At least 9 points (50%) of the Moodle test during the semester: There will be 3 Moodle tests. The test will appear after the 4th, 7th, and 10th lectures. The tests must be completed by 13th of December. Each test can be tried only once. For each test, there will be a 12-minutes time limit. Each test will be 6 points. It is not required to earn 50% for each test, but in total 9 points must be reached out of 18. If the requirement is not met at the end of the semester, there will be a paper-based test in the first week of the exam period with 18 questions, and again, 9 points must be reached. There will be oral exams. 4  SCHEDULE # Date Room Planned Topic 1. 2024. 09. 11. 14:15:00 ITK 320 Introduction, measurement, sensors 2. 2024. 09. 18. 14:15:00 ITK 320 Physical principles of sensing 3. 2024. 09. 25. 14:15:00 ITK 320 Human sensing 4. 2024. 10. 02. 14:15:00 ITK 320 Signal processing, biomedical signals 1st Moodle test available 5. 2024. 10. 09. 14:15:00 ITK 320 Biomimetic and bioinspired sensors 2024. 10. 16. 14:15:00 ITK 320 Pázmány Day 2024. 10. 23. 14:15:00 ITK 320 National holiday 2024. 10. 30. 14:15:00 ITK 320 Autumn break 6. 2024. 11. 06. 14:15:00 ITK 320 Bioinspired sensors: vision, hearing 7. 2024. 11. 13. 14:15:00 ITK 320 Bioinspired sensors: balance, olfaction, gustation 2nd Moodle test available 8. 2024. 11. 20. 14:15:00 ITK 320 Bioinspired sensors: touch 9. ITK 320 Measuring biomedical signals and examination 2024. 11. 27. 14:15:00 of the cardiovascular system 10. 2024. 12. 04. 14:15:00 ITK 320 Bioelectric sensors 3rd Moodle test available 11. 2024. 12. 11. 14:15:00 ITK 320 Wearable sensors 5  MEASUREMENT PRINCIPLES Measurement – The process of comparing an unknown quantity with a standard of the same quantity or standards of two or more related quantities Convert physical parameters to meaningful numbers Example: Measuring time and distance → Velocity Velocity Distance Time 6  MEASUREMENTS Importance: The advancement of science and technology is dependent upon a parallel progress in measurement techniques. Requirements of meaningful results: Accurately defined standard for comparison The measuring system and method must be proved Methods: Direct method: The unknown quantity is directly compared against a standard. Indirect method: The unknown quantity is indirectly compared against a standard using a proved method or system. Required if the direct method is not possible, feasible or practicable Usually less accurate and sensitive than the direct method. 7  UNITS OF MEASUREMENTS 8 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  TRANSDUCER A device that receives a signal in the form of one type of energy and converts it to a signal in another form A device that changes one form of energy into another Example 9  DEFINITION OF SENSOR 10 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  DEFINITION OF SENSOR It is a transducer which purpose is to sense or detect some characteristics of its environment. It is a transducer used to detect a parameter in one form and report it in electric signal. 11  ROLE OF SENSORS AND SENSOR SYSTEMS Measure (sense) unknown signals and parameters of an engineering or biological system and its environment Sensors are important in monitoring and learning about the system and possible interactions with its surrounding Sensor systems: A system of multiple sensors, including sensor network and sensor fusion A sensor and accessories that will be necessary in implementing it in a particular application (e.g. signal processing, data acquisition, data transmission/communication) 12  SENSOR TYPES Direct sensors A direct sensor converts a stimulus into an electrical signal or modifies an externally supplied electric signal. Example: photodiode Hybrid sensors A hybrid sensor in addition needs one or more transducers before a direct sensor can be employed to generate an electrical output Example: chemical sensors 13 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  SENSOR CLASSIFICATION Passive sensors It does not need any additional energy source and directly generates an electric signal in response to an external stimulus Example: thermocouple, photodiode, piezoelectric sensor Active sensors It requires external power for its operation, which is called an excitation signal. That signal is modified by the sensor to produce the output signal. Example: thermistor 14  SENSOR CLASSIFICATION Absolute sensor it detects a stimulus in reference to an absolute physical scale that is independent of the measurement conditions Example: thermistor, absolute pressure sensor in vacuum Relative sensor it produces a signal that relates to some special cases Example: thermocouple, relative pressure sensor in atmospheric pressure 15  SENSOR CLASSIFICATION Sensor specifications Sensitivity Stimulus range (span) Stability (short-, long-term) Resolution Accuracy Selectivity Speed of response Environmental conditions Overload characteristics Linearity Hysteresis Dead band Operating life Output format Cost, size, weight Other 16  SENSOR CLASSIFICATION Sensor material Inorganic Organic Conductor Insulator Semiconductor Liquid gas or plasma Biologic substance Other 17  SENSOR CLASSIFICATION Detection means used in sensors Conversion phenomena Biological Physical Chemical Thermoelectric, Photoelectric, Electric, magnetic or Photomagnetic, Magnetoelectric, electromagnetic Electromagnetic, Thermoelastic, Heat, temperature Electroelastic, Thermomagnetic, Thermooptic, Photoelastic, Other Mechanical displacement or Chemical wave Radioactivity, radiation Chemical transformation, Physical transformation, Electrochemical process spectroscopy, Other Biological Biochemical transformation, Physical transformation, Effect on test organism spectroscopy, Other 18  SENSOR CLASSIFICATION Fields of applications Stimulus Civil engineering Acoustic Disturbance, commerce, finance Biological Energy, power Chemical Health, medicine Electric Manufacturing Magnetic Military Optical Scientific measurement Mechanical Transportation Radiation Domestic appliances Thermal Environment, meteorology, security Information, telecommunication Marine Recreation, toys Space Other 19  SENSOR POSITIONS 20 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  SENSOR TRANSFER FUNCTION The transfer function represents the relation between stimulus (s) and response electrical signal (E) produced by the sensor. This relation can be written as E= f(s). Normally, stimulus (s) is unknown while the output signal E is measured. An inverse f –1(E) of the transfer function is required to compute the stimulus from the sensor’s measured response (E). 21 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  FUNCTIONAL APPROXIMATIONS The simplest transfer function is linear, given by: 𝐸 = 𝐴 + 𝐵𝑠 It represents a straight line with intercept A and slope B, which is called sensitivity (the larger B the greater influence of the stimulus) In many cases it is required to reference the sensor not to zero but to some more practical input value (s0): 𝐸 = 𝐸0 + 𝐵(𝑠 − 𝑠0 ) if E0 is a known sensor response of s0 22  FUNCTIONAL APPROXIMATIONS Non-linear transfer functions 1. Logarithmic function: 𝐸−𝐴 𝐸 = 𝐴 + 𝐵 ln(𝑠) 𝑠 =𝑒 𝐵 2. Exponential function: 𝑘𝑠 1 𝑬 𝐸 = 𝐴𝑒 𝑠 = ln 𝑘 𝐴 3. Power function: 𝑘 𝑘 𝐸−𝐴 𝐸 = 𝐴 + 𝐵𝑠 s = 𝐵 where A, B are parameters and k is the power factor 23  POLINOMIAL APPROXIMATIONS Useful if non of the above approximations fit sufficiently well Defined as a power series: 𝑚 𝑘𝑛 𝑛 𝐸 = 𝐴𝑒 𝑘𝑠 ≈𝐴෍ 𝑠 𝑛! 𝑛=0 24  LINEAR PIECEWISE APPROXIMATIONS Powerful method in a computerized data acquisition system Idea: break up a nonlinear transfer function of any shape into sections and consider each such section being linear An error of a piecewise approximation can be characterized by a maximum deviation δ of the approximation line from the real curve The knots do not need to be equally spaced. They should be closer to each other where nonlinearity is high and farther apart where nonlinearity is small. The larger the number of the knots the smaller the error 25 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  SPLINE INTERPOLATION In a similar way to a linear piecewise interpolation, the spline method is using different third-order polynomial interpolations between the selected experimental points called knots The most popular is cubic (third order) polynomials Curvature of a line at each point is defined by the second derivative It can preserve the smoothness of the transfer function More computational costs 26  MULTIDIMENSIONAL TRANSFER FUNCTION A sensor transfer function may depend on more than one input variable. Examples: Humidity sensor whose output depends on two input variables—relative humidity and temperature Thermal radiation (infrared) sensor. This function has two arguments–two temperatures: Tb – absolute temperature of an object of measurement Ts – absolute temperature of the sensing element V – the sensor’s output voltage G – a constant 𝑉 = 𝐺 𝑇𝑏4 − 𝑇𝑠4 27  MULTIDIMENSIONAL TRANSFER FUNCTION 28 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  SENSOR CALIBRATION Calibrate: to check, adjust or determine by comparison with a standard Calibration: comparison between measurements Sensor calibration: the relationship between the physical measurement variable and the signal variable A unique transfer function If tolerances of a sensor and interface circuit are broader than the required overall accuracy, a calibration of the sensor required 29  SENSOR CALIBRATION A calibration requires application of several precisely known stimuli and reading the corresponding sensor responses – calibration points Typically 2–5 calibration points are required to characterize a transfer function with higher accuracy To produce the calibration points, a standard reference source of the input stimuli is required Find the unknown coefficients (parameters) of the sensor transfer function so that the fully defined function can be employed during the measurement process to compute any stimulus in the desirable range, not only at the points used during the calibration 30  SENSOR CALIBRATION METHODS 1. Calculation of the transfer function or its approximation to fit the selected calibration points (curve fitting by computing coefficients of a selected approximation). 2. Adjustment of the data acquisition system to modify the measured data by making them to fit into a normalized or ideal transfer function. An example is scaling of the acquired data. 3. Modification of the sensor properties to fit the predetermined transfer function. 4. Creating a sensor-specific reference device with matching properties at particular calibrating points. 31 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  CALIBRATION ERROR Calibration error is the inaccuracy permitted when a sensor is calibrated in the factory. It has systematic nature → it is added to all possible real transfer functions. This error is not necessarily uniform over the range and may change depending on the type of error. Example: 32 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  COMPUTATION OF A STIMULUS A general goal of sensing is to determine the value of the input stimulus s from the measured output signal E. Methods: From an inverted transfer function s = F(E) that may be either an analytical or approximation function Linear Piecewise Approximation From a direct transfer function E = f(s) by use of an iterative computation. Iterative computation (Newton method) 𝑓 𝑠𝑖 − 𝐸 𝑠𝑖+1 = 𝑠𝑖 − 𝑓 ′ 𝑠𝑖 33  SENSOR CHARACTERISTICS Static characteristics Accuracy Resolution Precision Errors Sensitivity Linearity Hysteresis Saturation Dynamic characteristics Zero order systems First order systems Second order systems 34  STATIC CHARACTERISTICS DEFINITIONS Accuracy Accuracy is the capacity of a sensor to give results close to the true value of the measured quantity It is related to the bias of a set of measurements It is measured by the absolute and relative errors: Absolute error = Result – True value Relative error = Absolute error/True value 35 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  STATIC CHARACTERISTICS DEFINITIONS Resolution Resolution is the minimal change of the input necessary to produce a detectable change at the output Precision Precision is the capacity of a measuring instrument to give the same reading when repetitively measuring the same quantity under the same prescribed conditions. It implies agreement between successive readings, not closeness to the true value. It is related to the variance of a set of measurements. It is a necessary but not sufficient condition for accuracy. Repeatability Repeatability is the precision of a set of measurements taken over a short time interval. Reproducibility Reproducibility is the precision of a set of measurements, but taken over a long-time interval or performed by different operators or with different instruments or in different laboratories 36  STATIC CHARACTERISTICS DEFINITIONS Errors: Systematic errors: Interfering or modifying variables (e.g. temperature) Drift (e.g. changes in chemical structure or mechanical stresses) The measurement process changes the measurand (e.g. loading errors) The transmission process changes the signal (e.g. attenuation) Human observers (e.g. parallax errors) Systematic errors can be corrected with compensation methods (e.g. feedback, filtering) Random errors (Noise): A signal that carries no information. Sources of randomness: Repeatability of the measurand itself (e.g. height of a rough surface) Environment noise (e.g. background noise picked by a microphone) Transmission noise Signal to noise ratio (SNR) should be >> 1 37  SYSTEMATIC ERRORS VS. RANDOM ERRORS 38  STATIC CHARACTERISTICS DEFINITIONS Input range The maximum and minimum value of the physical variable that can be measured Output range Similar as the input range. Sensitivity The slope of the calibration curve. An ideal sensor will have a large and constant sensitivity. A nonlinear transfer function exhibits different sensitivities at different points, in this case the sensitivity is defined as a first derivative of the transfer function. 39  STATIC CHARACTERISTICS DEFINITIONS Linearity The closeness of the calibration curve to a specified straight line (e.g. theoretical behavior, least-squares fit). 40 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  STATIC CHARACTERISTICS DEFINITIONS Hysteresis The difference between two input values that correspond to the same output depending on the trajectory followed by the sensor 41 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  STATIC CHARACTERISTICS DEFINITIONS Saturation Every sensor has its operating limits. Above a limit of input stimuli the output signal no longer will be responsive. Also called span-end nonlinearity. 42 Reference: Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010.  DYNAMIC CHARACTERISTICS The sensor response to a variable input different from that exhibited when the input signals are constant (the latter is described by the static characteristics) The reason for dynamic characteristics is the presence of energy-storing elements: Inertial: masses, inductances Capacitances: electric, thermal Dynamic characteristics are determined by analyzing the response of the sensor to a family of variable input waveforms. Examples: impulse, step, ramp, sinusoidal, white noise 43  RELIABILITY Reliability is ability of a product/sensor to perform a required function under stated conditions for a stated period of time. It can be expressed in statistical terms as the probability that the device will function without failure over a specified time or a number of uses. Reliability specifies a failure – a temporary or permanent malfunction of a sensor. There is not any commonly accepted measure for sensor reliability. 44  SUMMARY – QUESTIONS What are the main principles of measurements? What do we call a transducer and a sensor? How can we classify the sensors? In a sensor system, what positions a sensor can be placed? What is the sensor transfer function? How can it be approximated? What is sensor calibration and why is it important? How a sensor can be calibrated? How a stimulus can be calculated? Which are the static and dynamic characteristics of sensors? How can these characteristics be defined? 45  REFERENCES 1. Fraden, Jacob. Handbook of modern sensors. Vol. 3. New York: Springer, 2010. 46