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Introduction 

Sinusoids 

Spectrum representation 

Sampling and aliasing 

FIR filters 

Frequency response of FIR filters 

The Z-transform 

IRR filters 

Continuous-time signals and LTI systems 

Frequency response 

Continuous-time fourier transform 

Filtering, modulation, and sampling 

Computing the spectrum 

Complex numbers 

Programming in Matlab 

Labaratory project 

Cr-rom demos


FIR filter (finite impulse response filter) --- Computer. Automation --- signaalprocessoren --- Electronics --- Z-transformatie --- digitale signalen --- digitale technieken --- DSP (digitale signaalprocessoren) --- FT (Fourier transformatie) --- filters --- Signal processing --- Multimedia systems. --- Traitement du signal --- Multimédia --- Digital techniques. --- Mathematics. --- Techniques numériques --- Mathématiques --- 621.38 --- Beeldverwerking --- Signaalverwerking --- dsp (digital signal processing) --- fir filter --- iir filter --- fourier transformatie --- matlab --- Multimédia --- Techniques numériques --- Mathématiques --- Multimedia systems --- Computer-based multimedia information systems --- Multimedia computing --- Multimedia information systems --- Multimedia knowledge systems --- Information storage and retrieval systems --- Digital signal processing --- Digital communications --- Digital electronics --- Digital techniques

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Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries.

Technology: general issues --- History of engineering & technology --- fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D–S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion --- n/a --- D-S evidence theory

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Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries.

fault detection --- deep learning --- transfer learning --- anomaly detection --- bearing --- wind turbines --- misalignment --- fault diagnosis --- information fusion --- improved artificial bee colony algorithm --- LSSVM --- D–S evidence theory --- optimal bandwidth --- kernel density estimation --- JS divergence --- domain adaptation --- partial transfer --- subdomain --- rotating machinery --- gearbox --- signal interception --- peak extraction --- cubic spline interpolation envelope --- combined fault diagnosis --- empirical wavelet transform --- grey wolf optimizer --- low pass FIR filter --- support vector machine --- satellite momentum wheel --- Huffman-multi-scale entropy (HMSE) --- support vector machine (SVM) --- adaptive particle swarm optimization (APSO) --- rail surface defect detection --- machine vision --- YOLOv4 --- MobileNetV3 --- multi-source heterogeneous fusion --- n/a --- D-S evidence theory

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The aim of this Special Issue is to explore new advanced solutions in electronic systems and interfaces to be employed in sensors, describing best practices, implementations, and applications. The selected papers in particular concern photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs) interfaces and applications, techniques for monitoring radiation levels, electronics for biomedical applications, design and applications of time-to-digital converters, interfaces for image sensors, and general-purpose theory and topologies for electronic interfaces.

Technology: general issues --- CMOS image sensor --- linearity --- adaptive nonlinear ramp --- fully differential pipeline --- double auto-zeroing --- high framerate --- fixed pattern noise --- floating diffusion --- readout scheme --- ramp generator circuit --- ultrasound --- PMUT --- high-voltage (HV) transmitter --- low-voltage receiver (RX) amplifier --- ultrasound application-specific integrated circuit (ASIC) --- monolithical integration --- CMOS --- MEMS --- electrical impedance spectroscopy (EIS) --- time-to-digital converter (TDC) --- time interpolator --- phase --- polar demodulator --- quantization --- reconfigurability --- current mode --- sensor interface --- silicon photomultiplier --- transimpedance amplifier --- voltage current conveyor --- field-programmable gate arrays (FPGA) --- non-uniform multiphase (NUMP) method --- temperature correction --- radiation sensor interface --- silicon photomultiplier (SiPM) --- mobile dosimeter --- analog-to-digital converter (ADC) --- magnetic bioreactor --- magnetoactive scaffolds --- tissue engineering --- magnetic actuator --- magnetoelectric stimulation --- selectable gain amplifier --- resistive-sensor --- current divider --- current reference --- front-end electronics --- single-photon response --- timing accuracy --- ultrasonic gas flowmeter --- the principle of time-difference method --- data filtering --- low-power measurement --- auto-balancing bridge method --- FIR filter --- FPGA --- impedance --- inductive-loop sensor --- multifrequency --- vehicle magnetic profile --- vector voltmeter --- signal processing --- background radiation monitoring system --- Atmel AVR ATmega328 microcontroller (MC) --- Geiger-Mueller counter --- Petri net model --- fifth-order low-pass filter --- operational transconductance amplifier --- multiple-input bulk-driven technique --- subthreshold region --- nanopower --- temperature compensation --- hysteresis --- quartz flexible accelerometer --- aerial inertial navigation system --- thermal effect --- creep effect --- electronic nose --- convolutional neural network --- component analysis --- xenon TPC --- trigger concepts --- data acquisition circuits --- n/a

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This book provides extensive information about advanced control techniques in electric drives. Multiple control and estimation methods are studied for position and speed tracking in different drives. Artificial intelligence tools, such as fuzzy logic and neural networks, are used for specific applications using electric drives.

History of engineering & technology --- PMSM drive --- current control --- deadbeat predictive control --- equivalent input disturbance --- BSAII --- Euclidean distance --- energy management --- E-REV --- overhead transmission line --- UAV inspection --- safe distance --- multi-source data fusion --- adaptive threshold --- permanent magnet synchronous motor --- second-order sliding mode control --- cascade control --- robustness --- PMSM --- model predictive control --- parameter identification --- hybrid electric vehicles (HEVs) --- mode transition --- adaptive sliding mode control (A-SMC) --- clutch actuator --- PI observer --- fractional order proportional-integral-differential (FOPID) --- indirect vector control --- position control of motor --- induction motor --- sensorless control --- sliding mode observer --- RBFNN-based self-tuning PID controller --- I-f startup strategy --- PMLSM --- position sensorless control --- high-frequency square-wave voltage injection --- FIR filter --- maglev train --- automotive electric powertrain --- rotor position sensor --- resolver --- inductive position sensor --- eddy current position sensor --- Hall sensor --- magnetoresistive position sensor --- Hall sensors --- brushless direct current motor drive system --- power electronics --- industrial application --- integrated electric drive system --- electromechanical coupling --- harmonic torque reduction strategy --- quantized --- nonlinear systems --- time delay --- lyapunov approach --- real-time implementation --- neural fuzzy controller --- I-f control strategy --- fractional order control --- synergetic control --- sliding mode control --- motor drives --- advanced control --- power converters --- estimation --- sensor --- artificial intelligence