Automated Number Plate Recognition (ANPR) uses camera-based optical character recognition on images of vehicle license plates to read the plates. ANPR was invented in 1976 in the UK and uses specialized CCTV cameras to detect license plates in real-time. Reasons for using ANPR include round-the-clock vehicle monitoring, reducing crime, easing toll management, and generating violations for traffic infractions.
The document summarizes a master's thesis presentation on real-time image processing using an Altera FPGA. It discusses using the FPGA to process high-resolution microscope images in real-time for feedback control. It presents the problem statement, theoretical background on FPGAs and image processing, and design and implementation of a system using the Altera Cyclone III FPGA board. The design implements a Nios II soft processor, video processing IP cores, and interfaces to DDR memory and DVI input/output. Future work focuses on improving system stability and migrating to the Zynq platform.
This document discusses ECG signal processing. It begins with an introduction to electrocardiograms and how they differ from EKGs. It then discusses how signal processing is important for ECGs and how ECGs operate based on three pulse waves. MATLAB functionality for ECG signal processing like FFTs and filtering is also covered. The document discusses various types of artefacts and noise sources that affect ECG signals. It outlines the objectives and methods of research which involve R-peak detection and notch filtering. Source code for these methods is also provided.
P-QRS-T peak detection of ECG signal by MATLABDiptaRoy2
This presentation summarizes a student project on detecting P-QRS-T peaks in an electrocardiogram (ECG) signal using MATLAB. The project aims to analyze ECG records and simulate the heart's electrical activity as represented by the P-wave, QRS complex, and T-wave. The presentation discusses why ECG is important, how ECG works, and the steps used to detect the peaks, including loading an ECG file into MATLAB, filtering the signal, and detecting peaks with functions like findpeaks. The goal is to help identify cardiovascular conditions by measuring peak intervals and comparing signals to databases of abnormalities.
This document describes an ECG monitoring system that uses a wireless sensor to monitor a patient's heartbeat and transmit the data via Wi-Fi. The system consists of a heartbeat sensor, transmitter, and receiver. The sensor measures the heartbeat which is transmitted wirelessly by the transmitter. The data can be received on an Android device and stored as a history. The system is intended to allow remote patient monitoring at an affordable price.
The document discusses processing and noise cancellation of electrocardiogram (ECG) signals. It begins by explaining what an ECG is and how it is generated by the electrical activity of the heart. The ECG provides information about heart rate and the strength of the heart muscles. ECG signals are recorded using skin electrodes and contain noise from sources like power lines and electrode contact that must be removed. Common processing techniques include filtering using bandpass and adaptive filters to reduce noise and enhance the ECG waveform. Further analysis of the filtered ECG can detect heart abnormalities and conditions. Adaptive noise cancellation algorithms use a reference noise signal to minimize interference in the primary ECG input signal.
Automated Number Plate Recognition (ANPR) uses camera-based optical character recognition on images of vehicle license plates to read the plates. ANPR was invented in 1976 in the UK and uses specialized CCTV cameras to detect license plates in real-time. Reasons for using ANPR include round-the-clock vehicle monitoring, reducing crime, easing toll management, and generating violations for traffic infractions.
The document summarizes a master's thesis presentation on real-time image processing using an Altera FPGA. It discusses using the FPGA to process high-resolution microscope images in real-time for feedback control. It presents the problem statement, theoretical background on FPGAs and image processing, and design and implementation of a system using the Altera Cyclone III FPGA board. The design implements a Nios II soft processor, video processing IP cores, and interfaces to DDR memory and DVI input/output. Future work focuses on improving system stability and migrating to the Zynq platform.
This document discusses ECG signal processing. It begins with an introduction to electrocardiograms and how they differ from EKGs. It then discusses how signal processing is important for ECGs and how ECGs operate based on three pulse waves. MATLAB functionality for ECG signal processing like FFTs and filtering is also covered. The document discusses various types of artefacts and noise sources that affect ECG signals. It outlines the objectives and methods of research which involve R-peak detection and notch filtering. Source code for these methods is also provided.
P-QRS-T peak detection of ECG signal by MATLABDiptaRoy2
This presentation summarizes a student project on detecting P-QRS-T peaks in an electrocardiogram (ECG) signal using MATLAB. The project aims to analyze ECG records and simulate the heart's electrical activity as represented by the P-wave, QRS complex, and T-wave. The presentation discusses why ECG is important, how ECG works, and the steps used to detect the peaks, including loading an ECG file into MATLAB, filtering the signal, and detecting peaks with functions like findpeaks. The goal is to help identify cardiovascular conditions by measuring peak intervals and comparing signals to databases of abnormalities.
This document describes an ECG monitoring system that uses a wireless sensor to monitor a patient's heartbeat and transmit the data via Wi-Fi. The system consists of a heartbeat sensor, transmitter, and receiver. The sensor measures the heartbeat which is transmitted wirelessly by the transmitter. The data can be received on an Android device and stored as a history. The system is intended to allow remote patient monitoring at an affordable price.
The document discusses processing and noise cancellation of electrocardiogram (ECG) signals. It begins by explaining what an ECG is and how it is generated by the electrical activity of the heart. The ECG provides information about heart rate and the strength of the heart muscles. ECG signals are recorded using skin electrodes and contain noise from sources like power lines and electrode contact that must be removed. Common processing techniques include filtering using bandpass and adaptive filters to reduce noise and enhance the ECG waveform. Further analysis of the filtered ECG can detect heart abnormalities and conditions. Adaptive noise cancellation algorithms use a reference noise signal to minimize interference in the primary ECG input signal.
Digital signal processing (DSP) involves analyzing, interpreting, and manipulating signals in a digital representation. DSP became prominent with advances in digital electronics and fast Fourier transform algorithms. Modern DSPs are optimized for multiply-accumulate operations and real-time processing using fixed-point arithmetic. The four biggest DSP manufacturers are Texas Instruments, Freescale, Lucent Technologies, and Analog Devices.
The document discusses automotive radar systems, including how they use radio signals to detect objects at a distance and help features like adaptive cruise control. It covers the components of a radar system like the antenna and processing unit, how radar detects objects through timing signals, and applications in driver assistance systems. Radar systems are becoming more important for advanced features in self-driving cars.
Isolation amplifiers provide electrical isolation and safety barriers between input and output circuits. They protect patients from leakage currents and break ohmic continuity between signals. There are three main methods for designing isolation amplifiers: transformer coupling, optical coupling, and capacitive coupling. Transformer coupled isolation amplifiers use an internal 20kHz oscillator, transformer, and rectifier/filter to provide isolated power supplies for each stage. Optically isolated amplifiers convert biological signals to light using an LED and back to signals using a phototransistor, providing improved patient safety. Capacitively coupled isolation amplifiers use digital encoding of the input voltage and frequency modulation, transmitting signals across a differential capacitive barrier with separate power supplies.
Complete pan tompkins implementation of ecg qrs detectorvanikeerthika
This document discusses the Pan-Tompkins algorithm for QRS detection in electrocardiogram (ECG) signals. It first provides background on ECG signals and their components (P, QRS, T waves). It then introduces two main methods for ECG detection, focusing on describing the Pan-Tompkins method. This method uses digital signal processing techniques like bandpass filtering, differentiation, and moving window averaging to identify QRS complexes based on their slope, amplitude, and width. The algorithm can reduce interference and automatically adjusts to changes in heart rate morphology.
Lecture Notes: EEEC6440315 Communication Systems - Inter Symbol Interference...AIMST University
This document discusses inter-symbol interference (ISI) that occurs when pulses transmitted through a band-limited channel spread into adjacent time slots, and various pulse shaping techniques to eliminate ISI. It explains that rectangular pulses cause ISI in practical band-limited channels, and introduces Nyquist's criterion for zero-ISI transmission. The document also describes raised cosine pulse shaping, which is commonly used when the symbol rate is less than the Nyquist rate, and provides an example of its use in WCDMA cellular systems.
Digital: Operating by the use of discrete signals to represent data in the form of numbers.
Signal: A parameter (Electrical quantity or effect) that can be varied in such a way as to convey information.
Processing: A series operations performed according to programmed instructions.
The following resources come from the 2009/10 BEng (Hons) in Digital Communications & Electronics (course number 2ELE0064) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
The objective of this module is to have built communication links using existing AM modulation, PSK modulation and demodulation blocks, constructed AM modulators and constructed PSK modulators using operational function blocks based on their mathematical expressions, and conducted simulations of the links and modulators, all in Simulink®.
This document provides information about electroencephalography (EEG), electromyography (EMG), and patient monitoring. It discusses how EEG is used to measure brain activity through electrodes on the scalp. It describes the different frequency bands seen on EEG and how they relate to mental states. The document outlines the components of an EEG recording system and various EEG artifacts. It also discusses EMG and how it is used to measure muscle electrical activity. Finally, it covers patient monitoring systems, including bedside monitors, central monitoring stations, and the parameters that are measured like heart rate, blood pressure, respiration rate.
The document outlines various topics related to biomedical instrumentation including biometrics, physiological systems of the human body like cardiovascular and respiratory systems, the kidney, bioelectric potentials, biopotential electrodes, and transducers for ECG, EEG, and EMG. It also provides details on the characteristics of biomedical instrumentation systems and describes concepts like bioelectric potential, action potential, and the recording setup for ECG, EEG, and EMG.
Respiration Sensor
The respiration sensor is a sensitive girth sensor worn using an easy fitting high durability woven elastic band fixed with a length-adjustable webbing belt. It detects chest or abdominal expansion/contraction and outputs the respiration waveform.
The document provides an overview of commonly used biomedical signals for monitoring physiological processes and detecting pathological conditions. It discusses several key signals including the electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electroretinogram (ERG), electrooculogram (EOG) and event-related potentials (ERPs). For each signal, it describes what physiological process is being measured, how the signal is recorded, its typical amplitude and bandwidth, main sources of interference and common applications. The document emphasizes that biomedical signals reflect the electrical, chemical and mechanical activities of cells, tissues and organs, and can provide important diagnostic information when analyzed.
The document discusses phonocardiography, which is the study of heart sounds using a phonocardiograph. A phonocardiograph is an instrument that picks up heart sounds, filters them, and displays the phonocardiogram, which is the graphic recording of heart sounds. There are two main types of heart sounds - normal sounds due to valve openings and closings, and abnormal murmurs due to turbulent blood flow. The document outlines the history and development of phonocardiography and the stethoscope, describes heart sound characteristics, and discusses the components of a phonocardiogram recording system.
This document summarizes digital modeling techniques for speech signals. It describes the vocal source and vocal tract that produce speech. It then discusses using sampling and techniques like PCM to digitally represent speech signals. Linear predictive coding is presented as a simple method to analyze speech that approximates samples as combinations of past signals. The summary concludes that linear prediction can be used for spectrum estimation by representing the vocal tract transfer function, pitch detection, and speech synthesis.
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Brati Sundar Nanda
This document discusses and compares various adaptive filtering algorithms for noise cancellation, including LMS, NLMS, RLS, and APA. It finds that RLS converges the fastest but has the highest complexity, while LMS converges the slowest but is simplest. NLMS and APA provide a balance between convergence speed and complexity. The document implements these algorithms on a noise cancellation problem and finds that RLS achieves the highest SNR improvement and best noise cancellation, followed by APA, NLMS, and LMS.
The document discusses speech recognition and voice recognition. It covers what voice is, the components of sound, why voices are different, classification of speech sounds, the speech production process, what voice recognition is, automatic speech recognition (ASR), types of ASR systems including speaker-dependent and speaker-independent, approaches to speech recognition including template matching and statistical approaches, and the process of speech recognition.
The document discusses artifact detection and removal in neural recordings. It defines artifacts as interfering signals originating from sources other than the brain that can obscure or distort the recorded neural signal. It describes common artifact sources like motion and electrode impedance changes. Artifact properties, detection techniques, and possible removal methods are examined, including filtering, slope measurement, and adaptive filtering. The document concludes some artifact removal methods are imperfect and loss of data can occur.
Speed detection cameras use Doppler radar to detect the speed of passing vehicles and automatically issue tickets to drivers exceeding the speed limit. They were first introduced in the 1980s in London and have since been widely adopted to reduce traffic accidents. Speed cameras work by transmitting radar beams and using the Doppler effect to measure the change in frequency of the reflected beams, from which they can calculate a vehicle's speed. Modern speed cameras provide accurate speed readings even in rain and can simultaneously detect multiple vehicles. While controversial, studies show speed cameras are effective at reducing speeding and promoting road safety when paired with public awareness campaigns.
The document discusses adaptive channel equalization using neural networks. It provides an overview of neural networks and their application to channel equalization. Specifically, it summarizes various neural network architectures that have been used for equalization, including multilayer perceptrons, functional link artificial neural networks, Chebyshev neural networks, and radial basis function networks. It compares the bit error rate performance of these different neural network equalizers with traditional linear equalizers such as LMS and RLS. Overall, the document finds that neural network equalizers can better handle nonlinear channel distortions compared to linear equalizers and that radial basis function networks provide particularly good performance for channel equalization applications.
Digital signal processing (DSP) involves analyzing, interpreting, and manipulating signals in a digital representation. DSP became prominent with advances in digital electronics and fast Fourier transform algorithms. Modern DSPs are optimized for multiply-accumulate operations and real-time processing using fixed-point arithmetic. The four biggest DSP manufacturers are Texas Instruments, Freescale, Lucent Technologies, and Analog Devices.
The document discusses automotive radar systems, including how they use radio signals to detect objects at a distance and help features like adaptive cruise control. It covers the components of a radar system like the antenna and processing unit, how radar detects objects through timing signals, and applications in driver assistance systems. Radar systems are becoming more important for advanced features in self-driving cars.
Isolation amplifiers provide electrical isolation and safety barriers between input and output circuits. They protect patients from leakage currents and break ohmic continuity between signals. There are three main methods for designing isolation amplifiers: transformer coupling, optical coupling, and capacitive coupling. Transformer coupled isolation amplifiers use an internal 20kHz oscillator, transformer, and rectifier/filter to provide isolated power supplies for each stage. Optically isolated amplifiers convert biological signals to light using an LED and back to signals using a phototransistor, providing improved patient safety. Capacitively coupled isolation amplifiers use digital encoding of the input voltage and frequency modulation, transmitting signals across a differential capacitive barrier with separate power supplies.
Complete pan tompkins implementation of ecg qrs detectorvanikeerthika
This document discusses the Pan-Tompkins algorithm for QRS detection in electrocardiogram (ECG) signals. It first provides background on ECG signals and their components (P, QRS, T waves). It then introduces two main methods for ECG detection, focusing on describing the Pan-Tompkins method. This method uses digital signal processing techniques like bandpass filtering, differentiation, and moving window averaging to identify QRS complexes based on their slope, amplitude, and width. The algorithm can reduce interference and automatically adjusts to changes in heart rate morphology.
Lecture Notes: EEEC6440315 Communication Systems - Inter Symbol Interference...AIMST University
This document discusses inter-symbol interference (ISI) that occurs when pulses transmitted through a band-limited channel spread into adjacent time slots, and various pulse shaping techniques to eliminate ISI. It explains that rectangular pulses cause ISI in practical band-limited channels, and introduces Nyquist's criterion for zero-ISI transmission. The document also describes raised cosine pulse shaping, which is commonly used when the symbol rate is less than the Nyquist rate, and provides an example of its use in WCDMA cellular systems.
Digital: Operating by the use of discrete signals to represent data in the form of numbers.
Signal: A parameter (Electrical quantity or effect) that can be varied in such a way as to convey information.
Processing: A series operations performed according to programmed instructions.
The following resources come from the 2009/10 BEng (Hons) in Digital Communications & Electronics (course number 2ELE0064) from the University of Hertfordshire. All the mini projects are designed as level two modules of the undergraduate programmes.
The objective of this module is to have built communication links using existing AM modulation, PSK modulation and demodulation blocks, constructed AM modulators and constructed PSK modulators using operational function blocks based on their mathematical expressions, and conducted simulations of the links and modulators, all in Simulink®.
This document provides information about electroencephalography (EEG), electromyography (EMG), and patient monitoring. It discusses how EEG is used to measure brain activity through electrodes on the scalp. It describes the different frequency bands seen on EEG and how they relate to mental states. The document outlines the components of an EEG recording system and various EEG artifacts. It also discusses EMG and how it is used to measure muscle electrical activity. Finally, it covers patient monitoring systems, including bedside monitors, central monitoring stations, and the parameters that are measured like heart rate, blood pressure, respiration rate.
The document outlines various topics related to biomedical instrumentation including biometrics, physiological systems of the human body like cardiovascular and respiratory systems, the kidney, bioelectric potentials, biopotential electrodes, and transducers for ECG, EEG, and EMG. It also provides details on the characteristics of biomedical instrumentation systems and describes concepts like bioelectric potential, action potential, and the recording setup for ECG, EEG, and EMG.
Respiration Sensor
The respiration sensor is a sensitive girth sensor worn using an easy fitting high durability woven elastic band fixed with a length-adjustable webbing belt. It detects chest or abdominal expansion/contraction and outputs the respiration waveform.
The document provides an overview of commonly used biomedical signals for monitoring physiological processes and detecting pathological conditions. It discusses several key signals including the electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electroretinogram (ERG), electrooculogram (EOG) and event-related potentials (ERPs). For each signal, it describes what physiological process is being measured, how the signal is recorded, its typical amplitude and bandwidth, main sources of interference and common applications. The document emphasizes that biomedical signals reflect the electrical, chemical and mechanical activities of cells, tissues and organs, and can provide important diagnostic information when analyzed.
The document discusses phonocardiography, which is the study of heart sounds using a phonocardiograph. A phonocardiograph is an instrument that picks up heart sounds, filters them, and displays the phonocardiogram, which is the graphic recording of heart sounds. There are two main types of heart sounds - normal sounds due to valve openings and closings, and abnormal murmurs due to turbulent blood flow. The document outlines the history and development of phonocardiography and the stethoscope, describes heart sound characteristics, and discusses the components of a phonocardiogram recording system.
This document summarizes digital modeling techniques for speech signals. It describes the vocal source and vocal tract that produce speech. It then discusses using sampling and techniques like PCM to digitally represent speech signals. Linear predictive coding is presented as a simple method to analyze speech that approximates samples as combinations of past signals. The summary concludes that linear prediction can be used for spectrum estimation by representing the vocal tract transfer function, pitch detection, and speech synthesis.
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Brati Sundar Nanda
This document discusses and compares various adaptive filtering algorithms for noise cancellation, including LMS, NLMS, RLS, and APA. It finds that RLS converges the fastest but has the highest complexity, while LMS converges the slowest but is simplest. NLMS and APA provide a balance between convergence speed and complexity. The document implements these algorithms on a noise cancellation problem and finds that RLS achieves the highest SNR improvement and best noise cancellation, followed by APA, NLMS, and LMS.
The document discusses speech recognition and voice recognition. It covers what voice is, the components of sound, why voices are different, classification of speech sounds, the speech production process, what voice recognition is, automatic speech recognition (ASR), types of ASR systems including speaker-dependent and speaker-independent, approaches to speech recognition including template matching and statistical approaches, and the process of speech recognition.
The document discusses artifact detection and removal in neural recordings. It defines artifacts as interfering signals originating from sources other than the brain that can obscure or distort the recorded neural signal. It describes common artifact sources like motion and electrode impedance changes. Artifact properties, detection techniques, and possible removal methods are examined, including filtering, slope measurement, and adaptive filtering. The document concludes some artifact removal methods are imperfect and loss of data can occur.
Speed detection cameras use Doppler radar to detect the speed of passing vehicles and automatically issue tickets to drivers exceeding the speed limit. They were first introduced in the 1980s in London and have since been widely adopted to reduce traffic accidents. Speed cameras work by transmitting radar beams and using the Doppler effect to measure the change in frequency of the reflected beams, from which they can calculate a vehicle's speed. Modern speed cameras provide accurate speed readings even in rain and can simultaneously detect multiple vehicles. While controversial, studies show speed cameras are effective at reducing speeding and promoting road safety when paired with public awareness campaigns.
The document discusses adaptive channel equalization using neural networks. It provides an overview of neural networks and their application to channel equalization. Specifically, it summarizes various neural network architectures that have been used for equalization, including multilayer perceptrons, functional link artificial neural networks, Chebyshev neural networks, and radial basis function networks. It compares the bit error rate performance of these different neural network equalizers with traditional linear equalizers such as LMS and RLS. Overall, the document finds that neural network equalizers can better handle nonlinear channel distortions compared to linear equalizers and that radial basis function networks provide particularly good performance for channel equalization applications.
Università Di Salerno Presentazione Tesi Gaetano Costaguest777bcf
Presentazione della Tesi di Laurea in Informatica "Editoria Online e Nuovi Media: un'Esperienza Lavorativa sull'Utilizzo delle Tecnologie Web 2.0" a cura di Gaetano Costa
This summary provides the key points from the document in 3 sentences:
The cooperative focuses on sustainable agriculture across various territories in Italy, working with farmers and local communities. It aims to promote local production and traditional methods, as well as autonomy and social values through collective management. The interview discusses the challenges of modernization and globalization, and how the cooperative seeks to address these through integrated practices supporting farmers, territories, and local economies.
Agro-ecology is defined as applying ecological principles to agriculture and food systems. It originated as a scientific concept but has expanded to include social and political dimensions. Agro-ecology aims to question dominant industrial agricultural models and globalized markets by promoting more sustainable practices. These include recycling biomass and nutrients, improving soils, reducing external inputs, optimizing biodiversity and interactions between ecosystem elements, integrating food production and environmental protection, and acknowledging both traditional and scientific knowledge. The principles of agro-ecology also emphasize participatory research, autonomy, and food sovereignty.
This newsletter provides updates on PAN Europe's projects regarding bees collapse, endocrine disrupting chemicals in food, and the Week Without Pesticides. It introduces new staff members Lucie Daniel and Isabelle Pinzauti and notes Martin Dermine has joined as the bee expert. It also summarizes a symposium on integrated pest management and sustainable agriculture held in Brussels. Articles discuss the need to reduce pesticide dependency and move towards more sustainable farming practices like crop rotation and legume production.
Presentazione Ufficiale Rilevazione frequenza cardiaca nel calcio
1. Facoltà di Medicina e Chirurgia Corso di Laurea in Scienze Motorie La rilevazione della frequenza cardiaca nel calcio Relatore Prof. Massimo Gulisano Candidato Alberto Fatticcioni Correlatore Prof. Mario Marella A.A. 2003004
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4. Frequenza cardiaca e modello prestativo In riferimento alle frequenze cardiache rilevate durante la gara è possibile individuare un ‘ modello prestativo ’ relativo al carico di lavoro tipico del calcio .
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11. FC e modello prestativo: lo studio del caso Risultati ed analisi dei dati 66.7% 76bpm (38.4%) 153bpm (77.3%) 196bpm (98.995) 2.38.30.1 2.38.30.1 7. Totale tempo di studio 81.5% 114bpm (57.6%) 173bpm (87.4%) 196bpm (98.99%) 0.49.15.0 2.35.15.0 6. II° tempo 39.25% 96bpm (48.5%) 116bpm (58.5%) 138bpm (69.5%) 0.09.45.0 1.46.00.0 5. Intervallo 80.75% 122bpm (61.2%) 172bpm (86.9%) 195bpm (98.5%) 0.50.00.0 1.36.15.0 4. I° tempo 42.22% 104bpm (52.5%) 120bpm (60.5%) 138bpm (69.5%) 0.13.00.0 0.46.15.0 3. Controllo arbitrale 59.5% 94bpm (47.5%) 143bpm (72%) 181bpm (91.5%) 0.19.35.0 0.33.15.0 2. Periodo di riscaldamento 22% 76bpm (38.4%) 93bpm (47%) 116bpm (58.6%) 0.13.40.0 0.13.40.0 1. Prima del riscaldamento %CC FC MIN E %FC MIN FC MEDIA E %FC MEDIA FCF MAX E %FC MAX TEMPO DI FRAZIONE TEMPO FRAZIONE