What is noise?
White noise
Impulsive noise
Active noise Cancellation
Car is a noisy environment: noise level between 65 and 75 db
Adaptive Noise Cancellation
Adaptive filter
Algorithms used to adjust the coefficients of the digital filter
The document discusses active noise cancellation and noise reduction techniques. It describes how active noise cancellation works by generating a sound wave with equal amplitude but opposite phase to the original noise, cancelling it out. Adaptive filters are used, with algorithms like LMS and RLS, to analyze input sounds and adjust filter coefficients to minimize noise. Applications include headphones, vehicles, aircraft, and noise-cancelling devices that can reduce ambient sounds.
This document discusses adaptive noise cancellation using the least mean squares (LMS) algorithm. It begins by introducing limitations of fixed filters for time-varying noise frequencies and overlapping signal and noise bands. It then defines digital filters, noise cancellation, adaptive filters, and adaptive noise cancellation. The LMS algorithm is described as consisting of a filtering process and adaptive process to minimize the mean square of the error signal. Code is presented to implement the initial part, main body, and display results of an adaptive noise cancellation system using LMS. Applications are identified in echo and noise cancellation, acoustic echo cancellation, system identification, and noise removal from ECG signals.
This document discusses using an adaptive filter for noise cancellation in a laboratory duct. It aims to design and implement an active noise control system using a feedforward topology with an adaptive filter. Active noise control introduces a secondary anti-noise source to destructively interfere with and cancel primary noise. Adaptive filters automatically adjust their filter coefficients using algorithms like the least mean squares algorithm to optimize noise cancellation in response to changing environments. The proposed approach would implement this active noise cancellation system using a laboratory duct model and an adaptive filtered-X algorithm.
Noise cancellation and suppression techniques use signal processing to extract useful information from a mixture of signals. Noise cancellation works by passing a corrupted signal through a filter to suppress noise while preserving the original signal. Active noise cancellation systems emit an inverted phase signal to destructively interfere with and cancel out the original noise signal. Noise can be modeled as white noise, colored noise, impulsive noise, or acoustic echo. White noise suppression techniques use quantization and filter banks. Impulsive noise compensation uses non-linear limiting. Acoustic echo cancellation uses subband analysis and synthesis with adaptation to model and subtract the echo from microphone signals.
Active noise cancellation uses a microphone to measure ambient noise and generate an inverted "anti-noise" signal to destructively interfere with and cancel out the noise. It works best for low frequencies while passive noise control using insulation is more effective at higher frequencies. Adaptive noise cancellation algorithms like LMS analyze noise waveforms and generate inverted signals through transducers to reduce perceived noise levels. Noise-cancelling headphones apply this technique to improve listening and sleep on planes by offsetting engine noise.
The document provides an overview of adaptive filters. It discusses that adaptive filters are digital filters that have self-adjusting characteristics to changes in input signals. They have two main components: a digital filter with adjustable coefficients and an adaptive algorithm. Common adaptive algorithms are LMS and RLS. Adaptive filters are used for applications like noise cancellation, system identification, channel equalization, and signal prediction. The key aspects of adaptive filter theory and algorithms like LMS, RLS, Wiener filters are also covered.
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.
These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
The document discusses active noise cancellation and noise reduction techniques. It describes how active noise cancellation works by generating a sound wave with equal amplitude but opposite phase to the original noise, cancelling it out. Adaptive filters are used, with algorithms like LMS and RLS, to analyze input sounds and adjust filter coefficients to minimize noise. Applications include headphones, vehicles, aircraft, and noise-cancelling devices that can reduce ambient sounds.
This document discusses adaptive noise cancellation using the least mean squares (LMS) algorithm. It begins by introducing limitations of fixed filters for time-varying noise frequencies and overlapping signal and noise bands. It then defines digital filters, noise cancellation, adaptive filters, and adaptive noise cancellation. The LMS algorithm is described as consisting of a filtering process and adaptive process to minimize the mean square of the error signal. Code is presented to implement the initial part, main body, and display results of an adaptive noise cancellation system using LMS. Applications are identified in echo and noise cancellation, acoustic echo cancellation, system identification, and noise removal from ECG signals.
This document discusses using an adaptive filter for noise cancellation in a laboratory duct. It aims to design and implement an active noise control system using a feedforward topology with an adaptive filter. Active noise control introduces a secondary anti-noise source to destructively interfere with and cancel primary noise. Adaptive filters automatically adjust their filter coefficients using algorithms like the least mean squares algorithm to optimize noise cancellation in response to changing environments. The proposed approach would implement this active noise cancellation system using a laboratory duct model and an adaptive filtered-X algorithm.
Noise cancellation and suppression techniques use signal processing to extract useful information from a mixture of signals. Noise cancellation works by passing a corrupted signal through a filter to suppress noise while preserving the original signal. Active noise cancellation systems emit an inverted phase signal to destructively interfere with and cancel out the original noise signal. Noise can be modeled as white noise, colored noise, impulsive noise, or acoustic echo. White noise suppression techniques use quantization and filter banks. Impulsive noise compensation uses non-linear limiting. Acoustic echo cancellation uses subband analysis and synthesis with adaptation to model and subtract the echo from microphone signals.
Active noise cancellation uses a microphone to measure ambient noise and generate an inverted "anti-noise" signal to destructively interfere with and cancel out the noise. It works best for low frequencies while passive noise control using insulation is more effective at higher frequencies. Adaptive noise cancellation algorithms like LMS analyze noise waveforms and generate inverted signals through transducers to reduce perceived noise levels. Noise-cancelling headphones apply this technique to improve listening and sleep on planes by offsetting engine noise.
The document provides an overview of adaptive filters. It discusses that adaptive filters are digital filters that have self-adjusting characteristics to changes in input signals. They have two main components: a digital filter with adjustable coefficients and an adaptive algorithm. Common adaptive algorithms are LMS and RLS. Adaptive filters are used for applications like noise cancellation, system identification, channel equalization, and signal prediction. The key aspects of adaptive filter theory and algorithms like LMS, RLS, Wiener filters are also covered.
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.
These slides deal with the basic problem of channel equalization and exposes the issue related to it and shows how it can be balanced by the usage of effective and robust algorithms.
MFCCs were the standard feature for automatic speech recognition systems using HMM classifiers. MFCCs work by framing an audio signal, calculating the power spectrum of each frame, applying a Mel filterbank to group frequencies, taking the logarithm of the filterbank energies, and computing the DCT to decorrelate the features. The Mel scale relates perceived pitch to actual frequency in a way that matches human hearing. MFCCs were effective for GMM-HMM systems and helped speech recognition performance by representing audio signals in a way aligned with human perception.
The document discusses speech processing and vocoding. It begins by defining speech and how it is produced, including voiced and unvoiced sounds. It then describes the human speech production system and various speech coding techniques like waveform coding, vocoding, and analysis-by-synthesis coding. Finally, it provides details on the G.729 speech codec, including its operations, process flow, specifications, and how it achieves speech compression to 8 kbps from the original 128 kbps.
Isolation amplifiers provide electrical isolation and safety barriers between input and output stages. They use transformer, optical, or capacitive isolation methods and isolated power supplies to break continuity while amplifying low-level signals. Common applications include medical equipment, industrial processes, and data acquisition where electrical isolation is needed to protect patients or eliminate noise.
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
parametric method of power spectrum Estimationjunjer
The document discusses parametric methods of power spectrum estimation. It explains that parametric methods estimate the parameters of a mathematical model that describes the signal generation process. This involves selecting a model such as autoregressive (AR), moving average (MA), or autoregressive moving average (ARMA), estimating the model parameters from the data, and then using the estimated parameters to calculate the power spectrum. The document provides details on how to estimate the power spectrum using AR, MA, and ARMA models. It also discusses maximum entropy spectral estimation and high-resolution spectral estimation based on eigen-analysis.
A PLL or phase-locked loop is a control system that generates an output signal whose phase is related to the phase of an input signal. It consists of three basic elements: a phase detector that compares the phase of two signals and generates an error signal, a loop filter that filters the error signal, and a voltage-controlled oscillator whose frequency is controlled by the filtered error signal. PLLs are commonly used in applications such as frequency synthesis, signal demodulation, and motor speed control.
This document provides an introduction to equalization and summarizes several equalization techniques:
1) Zero forcing equalizers aim to completely eliminate intersymbol interference by inverting the channel response but can amplify noise.
2) The mean square error criterion aims to minimize the error between the received and desired signals when filtered by the equalizer. This can be solved using least squares or adaptive algorithms like LMS.
3) The least mean square algorithm approximates the steepest descent method to iteratively and adaptively update the equalizer filter taps to minimize the mean square error based only on instantaneous measurements. This makes it suitable for time-varying channels.
This document discusses key characteristics and concepts related to radio receivers. It covers sensitivity, selectivity, fidelity, noise figure, image frequency rejection, double spotting, tracking and alignment. Sensitivity refers to a receiver's ability to amplify weak signals and is determined by factors like noise power, receiver noise figure, and amplifier gain. Selectivity is a receiver's ability to differentiate the desired signal from unwanted signals, and depends on tuned circuit quality factor. Fidelity measures how accurately a receiver can reproduce the original signal. Noise figure is the ratio of input signal-to-noise ratio to output signal-to-noise ratio. Image frequency rejection and tracking/alignment are also summarized.
This document provides an overview of digital filter design. It introduces finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are designed using window techniques like rectangular, Hamming, and Kaiser windows. IIR filters are designed using approximation methods like Butterworth, Chebyshev I, and Chebyshev II. MATLAB code is provided to design low pass, high pass, and other filters using different window and approximation techniques. Pros and cons of FIR and IIR filters are discussed along with references.
This document discusses audio spotlighting technology which uses ultrasonic energy to create narrow beams of sound similar to light beams. It exploits the non-linearity of air to generate audible sound from inaudible ultrasound, producing a highly directional sound beam. The technology was pioneered in the 1960s for sonar but more recent developments have improved directivity and reduced distortion. It works by modulating audio signals onto ultrasonic carriers, which generate audible sound through nonlinear interactions in air as they propagate. This allows sound to be focused onto a specific area without others nearby hearing it.
The document provides information about the course EC6007 SPEECH PROCESSING. It outlines the course objectives which include enabling students to learn fundamentals of speech sounds, analyze speech parameters using various methods, equip students with speech modelling techniques, and gain knowledge of speech recognition and synthesis systems. The course outcomes expect students to be able to explain speech fundamentals, analyze speech parameters, apply speech models, explain speech recognition systems, and apply speech synthesis techniques. It also provides details about the various units and topics covered in the course.
Windowing techniques of fir filter designRohan Nagpal
Windowing techniques are used in FIR filter design to convert an infinite impulse response to a finite impulse response. The process involves choosing a desired frequency response, taking the inverse Fourier transform to get the impulse response, multiplying the impulse response by a window function, and realizing the filter. Common window functions include rectangular, Hanning, Hamming, and Blackman windows, which are selected based on the required stopband attenuation. The windowing technique allows designing FIR filters with a simple process but lacks flexibility compared to other design methods.
This document discusses frequency modulation (FM) and its types: phase modulation and frequency modulation. It describes the key characteristics of FM including its constant amplitude, higher signal-to-noise ratio, and infinite bandwidth. FM is classified as narrowband FM (NBFM) or wideband FM (WBFM) based on the modulation index. The document also covers pre-emphasis and de-emphasis circuits, methods for generating NBFM and WBFM signals including the direct and indirect (Armstrong's) methods.
The document discusses FM demodulation using a phase-locked loop (PLL). A PLL consists of a phase detector, loop filter, and voltage-controlled oscillator (VCO) connected in a feedback loop. It works by using the phase detector to compare the input signal frequency to the VCO output frequency. Any difference or error signal is fed through the loop filter to control the VCO frequency, adjusting it until the two frequencies are synchronized and phase-locked. In this way, a PLL can track the frequency and phase of an incoming FM signal to demodulate it.
This document summarizes a presentation on FIR and IIR filter design techniques. It introduces common IIR filter design methods like impulse invariance and bilinear transformation. It also discusses FIR filter design using window functions, frequency sampling, and minimizing mean squared error. Specific window functions are examined, including rectangular, triangular, Hanning, Hamming, Kaiser, and Blackman windows. The document provides an overview of digital filter design topics and serves as a reference for further exploration of FIR and IIR filter design methods.
This document provides an overview of decimation and interpolation in multirate signal processing. It discusses downsampling by an integer factor M, which reduces the sampling rate by taking every M-th sample and discarding the rest. Downsampling can cause aliasing if the signal is not bandlimited, so a low-pass filter is used beforehand. The document also covers properties like linearity and time-variance, identities for cascading systems, and polyphase decomposition to more efficiently implement decimation filters when the number of coefficients is a multiple of the decimation factor. Examples and illustrations are provided using MATLAB code.
Pulse code modulation (PCM) involves sampling an analog signal at regular intervals, quantizing the sample values, and encoding the samples as digital code. The analog voice signal is sampled 8000 times per second, with each sample represented by an 8-bit binary number. This results in a digital data rate of 64,000 bits per second to represent the original voice signal. Quantization assigns the sample values to discrete levels, introducing quantization error between the original and encoded signals.
1. Equalizers are used to reduce inter-symbol interference in wireless communication and help reduce bit errors at the receiver.
2. There are two main types of equalizers - linear equalizers and non-linear equalizers. Linear equalizers include zero forcing and MMSE equalizers, while non-linear equalizers include decision feedback equalizers.
3. Adaptive equalizers automatically adapt to changing channel properties over time using algorithms like LMS and RLS to update equalizer coefficients.
Noise pollution can negatively impact human health and quality of life. Unwanted sound that is loud or persistent enough can damage hearing or cause annoyance. Common sources of noise pollution include transportation like vehicles, aircraft, and trains, as well as industrial operations, construction, and recreational activities. The ear converts sound vibrations into nerve signals that are perceived by the brain as sound. Noise is measured in units called decibels and standards aim to limit exposure and emissions from various sources.
The OSHA standard for noise requires at-workers to receive training on how noise affects them along with the controls to protect them from exposure and monitor their hearing. If this the type of training that you require to meet your regulatory obligations, contact us at The Windsor Consulting Group, Inc. We have over 60 occupational health and safety course offering to help your workforce, public, and the environment
MFCCs were the standard feature for automatic speech recognition systems using HMM classifiers. MFCCs work by framing an audio signal, calculating the power spectrum of each frame, applying a Mel filterbank to group frequencies, taking the logarithm of the filterbank energies, and computing the DCT to decorrelate the features. The Mel scale relates perceived pitch to actual frequency in a way that matches human hearing. MFCCs were effective for GMM-HMM systems and helped speech recognition performance by representing audio signals in a way aligned with human perception.
The document discusses speech processing and vocoding. It begins by defining speech and how it is produced, including voiced and unvoiced sounds. It then describes the human speech production system and various speech coding techniques like waveform coding, vocoding, and analysis-by-synthesis coding. Finally, it provides details on the G.729 speech codec, including its operations, process flow, specifications, and how it achieves speech compression to 8 kbps from the original 128 kbps.
Isolation amplifiers provide electrical isolation and safety barriers between input and output stages. They use transformer, optical, or capacitive isolation methods and isolated power supplies to break continuity while amplifying low-level signals. Common applications include medical equipment, industrial processes, and data acquisition where electrical isolation is needed to protect patients or eliminate noise.
this ppts deal with adaptive noise cancellation using normalized least mean fourth algorithm and mean square comparison for both normalized least mean square algorithm and least mean fourth algorithm with gaussian, binary and unifrom signals as inputs.
parametric method of power spectrum Estimationjunjer
The document discusses parametric methods of power spectrum estimation. It explains that parametric methods estimate the parameters of a mathematical model that describes the signal generation process. This involves selecting a model such as autoregressive (AR), moving average (MA), or autoregressive moving average (ARMA), estimating the model parameters from the data, and then using the estimated parameters to calculate the power spectrum. The document provides details on how to estimate the power spectrum using AR, MA, and ARMA models. It also discusses maximum entropy spectral estimation and high-resolution spectral estimation based on eigen-analysis.
A PLL or phase-locked loop is a control system that generates an output signal whose phase is related to the phase of an input signal. It consists of three basic elements: a phase detector that compares the phase of two signals and generates an error signal, a loop filter that filters the error signal, and a voltage-controlled oscillator whose frequency is controlled by the filtered error signal. PLLs are commonly used in applications such as frequency synthesis, signal demodulation, and motor speed control.
This document provides an introduction to equalization and summarizes several equalization techniques:
1) Zero forcing equalizers aim to completely eliminate intersymbol interference by inverting the channel response but can amplify noise.
2) The mean square error criterion aims to minimize the error between the received and desired signals when filtered by the equalizer. This can be solved using least squares or adaptive algorithms like LMS.
3) The least mean square algorithm approximates the steepest descent method to iteratively and adaptively update the equalizer filter taps to minimize the mean square error based only on instantaneous measurements. This makes it suitable for time-varying channels.
This document discusses key characteristics and concepts related to radio receivers. It covers sensitivity, selectivity, fidelity, noise figure, image frequency rejection, double spotting, tracking and alignment. Sensitivity refers to a receiver's ability to amplify weak signals and is determined by factors like noise power, receiver noise figure, and amplifier gain. Selectivity is a receiver's ability to differentiate the desired signal from unwanted signals, and depends on tuned circuit quality factor. Fidelity measures how accurately a receiver can reproduce the original signal. Noise figure is the ratio of input signal-to-noise ratio to output signal-to-noise ratio. Image frequency rejection and tracking/alignment are also summarized.
This document provides an overview of digital filter design. It introduces finite impulse response (FIR) and infinite impulse response (IIR) filters. FIR filters are designed using window techniques like rectangular, Hamming, and Kaiser windows. IIR filters are designed using approximation methods like Butterworth, Chebyshev I, and Chebyshev II. MATLAB code is provided to design low pass, high pass, and other filters using different window and approximation techniques. Pros and cons of FIR and IIR filters are discussed along with references.
This document discusses audio spotlighting technology which uses ultrasonic energy to create narrow beams of sound similar to light beams. It exploits the non-linearity of air to generate audible sound from inaudible ultrasound, producing a highly directional sound beam. The technology was pioneered in the 1960s for sonar but more recent developments have improved directivity and reduced distortion. It works by modulating audio signals onto ultrasonic carriers, which generate audible sound through nonlinear interactions in air as they propagate. This allows sound to be focused onto a specific area without others nearby hearing it.
The document provides information about the course EC6007 SPEECH PROCESSING. It outlines the course objectives which include enabling students to learn fundamentals of speech sounds, analyze speech parameters using various methods, equip students with speech modelling techniques, and gain knowledge of speech recognition and synthesis systems. The course outcomes expect students to be able to explain speech fundamentals, analyze speech parameters, apply speech models, explain speech recognition systems, and apply speech synthesis techniques. It also provides details about the various units and topics covered in the course.
Windowing techniques of fir filter designRohan Nagpal
Windowing techniques are used in FIR filter design to convert an infinite impulse response to a finite impulse response. The process involves choosing a desired frequency response, taking the inverse Fourier transform to get the impulse response, multiplying the impulse response by a window function, and realizing the filter. Common window functions include rectangular, Hanning, Hamming, and Blackman windows, which are selected based on the required stopband attenuation. The windowing technique allows designing FIR filters with a simple process but lacks flexibility compared to other design methods.
This document discusses frequency modulation (FM) and its types: phase modulation and frequency modulation. It describes the key characteristics of FM including its constant amplitude, higher signal-to-noise ratio, and infinite bandwidth. FM is classified as narrowband FM (NBFM) or wideband FM (WBFM) based on the modulation index. The document also covers pre-emphasis and de-emphasis circuits, methods for generating NBFM and WBFM signals including the direct and indirect (Armstrong's) methods.
The document discusses FM demodulation using a phase-locked loop (PLL). A PLL consists of a phase detector, loop filter, and voltage-controlled oscillator (VCO) connected in a feedback loop. It works by using the phase detector to compare the input signal frequency to the VCO output frequency. Any difference or error signal is fed through the loop filter to control the VCO frequency, adjusting it until the two frequencies are synchronized and phase-locked. In this way, a PLL can track the frequency and phase of an incoming FM signal to demodulate it.
This document summarizes a presentation on FIR and IIR filter design techniques. It introduces common IIR filter design methods like impulse invariance and bilinear transformation. It also discusses FIR filter design using window functions, frequency sampling, and minimizing mean squared error. Specific window functions are examined, including rectangular, triangular, Hanning, Hamming, Kaiser, and Blackman windows. The document provides an overview of digital filter design topics and serves as a reference for further exploration of FIR and IIR filter design methods.
This document provides an overview of decimation and interpolation in multirate signal processing. It discusses downsampling by an integer factor M, which reduces the sampling rate by taking every M-th sample and discarding the rest. Downsampling can cause aliasing if the signal is not bandlimited, so a low-pass filter is used beforehand. The document also covers properties like linearity and time-variance, identities for cascading systems, and polyphase decomposition to more efficiently implement decimation filters when the number of coefficients is a multiple of the decimation factor. Examples and illustrations are provided using MATLAB code.
Pulse code modulation (PCM) involves sampling an analog signal at regular intervals, quantizing the sample values, and encoding the samples as digital code. The analog voice signal is sampled 8000 times per second, with each sample represented by an 8-bit binary number. This results in a digital data rate of 64,000 bits per second to represent the original voice signal. Quantization assigns the sample values to discrete levels, introducing quantization error between the original and encoded signals.
1. Equalizers are used to reduce inter-symbol interference in wireless communication and help reduce bit errors at the receiver.
2. There are two main types of equalizers - linear equalizers and non-linear equalizers. Linear equalizers include zero forcing and MMSE equalizers, while non-linear equalizers include decision feedback equalizers.
3. Adaptive equalizers automatically adapt to changing channel properties over time using algorithms like LMS and RLS to update equalizer coefficients.
Noise pollution can negatively impact human health and quality of life. Unwanted sound that is loud or persistent enough can damage hearing or cause annoyance. Common sources of noise pollution include transportation like vehicles, aircraft, and trains, as well as industrial operations, construction, and recreational activities. The ear converts sound vibrations into nerve signals that are perceived by the brain as sound. Noise is measured in units called decibels and standards aim to limit exposure and emissions from various sources.
The OSHA standard for noise requires at-workers to receive training on how noise affects them along with the controls to protect them from exposure and monitor their hearing. If this the type of training that you require to meet your regulatory obligations, contact us at The Windsor Consulting Group, Inc. We have over 60 occupational health and safety course offering to help your workforce, public, and the environment
The document discusses various aspects of indoor and outdoor acoustics. It covers topics like sound principles, studio acoustics, live rooms, dead rooms, surface types, reverberation, soundproofing, sound bites, presence, unwanted noise, noise gates, and unwanted ambience. It provides information on how sound behaves in different environments and what techniques are used to control sounds.
This document discusses various technologies for noise reduction and hearing assistance, including spectral subtraction, FM technologies, infrared systems, induction loop technologies, active noise cancellation, and hearing aids. It provides statistics on hearing loss and hearing aid usage in several European countries. It also includes information on noise levels, common causes of hearing loss, and the percentage of Americans with high frequency hearing loss due to noise exposure.
About 30 million workers are exposed to hazardous noise on the job. One in 4 of these workers (or 7.5 million Americans) will develop permanent hearing loss.Noise-induced hearing loss is the most common occupational hazard for American workers.Hearing loss from noise is slow and painless; you can have a disability before you notice it.If you must raise your voice to speak with someone only 3 feet away, you are in high (hazardous) noise. It is 100% preventable
This document discusses noise and its impacts. It defines noise as any unwanted sound and notes that noise levels are rising, becoming an environmental stressor. At low levels, noise can be annoying but at higher levels it can damage hearing or interfere with speech. Chronic noise exposure is associated with hearing loss and cardiovascular effects like increased blood pressure. Noise pollution also causes annoyance, sleep disturbances, and other health issues. Sources of noise include transportation like aircraft, highways, and railways, as well as industrial and recreational activities. Noise exposure can impair task performance and attention. The document reviews evidence on noise's impacts on health, behavior, and mental health.
Noise pollution can come from various sources like transportation, construction, and industrial activities. It is measured in decibels and exposure to loud noise over 85dB can cause hearing damage over time. Noise affects humans physically and psychologically, reducing sleep quality and increasing stress. It also harms animal communication and habitats. Methods to reduce noise include better urban planning, equipment modifications, noise barriers, and controlling vehicle speed. Regulations establish limits and standards provide guidelines but enforcement can be improved.
The document discusses noise pollution, including its measurement, sources, effects, and control. It defines sound and noise, and explains how sound is measured in units such as frequency, intensity, and decibels. Common sources of noise pollution like traffic, construction, and industrial activities are identified. The effects of noise on hearing, health, communication, and work are outlined. Standards for acceptable noise limits in different areas are provided. Finally, the document discusses approaches to control noise pollution through modifications to noise sources, transmission paths, and receivers.
Noise pollution can negatively impact human health and the environment. It is caused by loud sounds from sources like vehicles, construction sites, and industrial activity. Prolonged exposure to noise above safe levels can lead to hearing loss, cardiovascular issues, and disrupted sleep. Methods to reduce noise pollution include using barriers around noisy machinery, limiting vehicle speeds, planting trees as buffers, and protecting workers' hearing with earplugs. Controlling noise at the source is the most effective approach.
Noise pollution comes from both transportation and human sources and can negatively impact both human and animal health. Unwanted sounds become noise pollution when they unreasonably disturb daily life through distraction, sleep disruption, and inability to control the source of the sound. Noise pollution is measured in decibels and common sources include traffic, construction, aircraft, loud machinery, and barking dogs. Prolonged exposure to noise above 85dB can cause hearing loss and other health issues like high blood pressure and mental health problems. Wildlife are also affected through hearing damage, inability to communicate, and changes in behavior and reproduction. Regulations aim to limit noise during certain hours and require mitigation efforts to reduce impacts.
This document discusses noise and hearing conservation. It begins by defining sound and noise, and how they are measured. It then covers topics like hearing loss, noise exposure limits, and strategies for noise control including engineering controls, administrative controls, and hearing protectors. The key aspects are measuring noise levels, understanding how excessive noise can damage hearing, and implementing controls and protections to prevent occupational hearing loss.
Noise pollution arises from various sources like vehicles, construction machinery, and household appliances. It is defined as unwanted sound that disrupts human or animal activity. Sound is measured in frequency (cycles/second), intensity (sound energy/second), and decibel level which is a logarithmic ratio of measured to reference intensity. The human ear can detect sounds from 20-20,000 Hz. Noise pollution has physiological effects like hearing loss, and psychological effects like sleep disturbances and annoyance. Control methods include using mufflers and insulation to reduce sound transmission, and ear protection for those working in noisy environments.
Noise Pollution is defined as any undesirable human or machine created noise which disturbs human or animal activity or balance. It is measured in decibels (dB) which is a logarithmic ratio of sound pressures. Prolonged exposure to loud noise can cause permanent hearing loss, annoyance, stress, and disrupted sleep and human performance. Noise pollution affects animals through hearing loss, physiological stress, and disruption of behavior and ecosystems. Plants are also impacted through reduced growth. Control measures include reducing noise at the source through improved machinery and mufflers, zoning of industrial areas away from homes, sound insulation of buildings, use of ear protection, legislative bans on loud noises, and planting trees.
The document discusses various acoustics principles and concepts related to indoor and outdoor sound recording. It covers how sound travels, different room types used for recording like live rooms and dead rooms, surface types that impact sound reflection and absorption, and challenges of outdoor recording like dealing with wind noise and ambient noise. It provides examples of techniques used to reduce unwanted noise, like using directional microphones, windshields, isolation panels, and positioning microphones away from noise sources.
Noise pollution can negatively impact human health and the environment in several ways. It is defined as unwanted and disturbing sounds that disrupt normal activities or cause harm. Common sources include vehicles, construction equipment, and industrial operations. Effects range from temporary hearing loss and sleep disruption to long-term cardiovascular problems. Reducing noise pollution involves identifying sound sources, modifying noisy machinery, adding sound barriers, and providing protective equipment for workers.
Noise pollution can negatively impact human health and the environment in several ways. It is defined as unwanted and disturbing sounds that disrupt normal activities or cause harm. Common sources include vehicles, construction, and industrial operations. Effects range from temporary hearing loss and sleep disruption to long-term cardiovascular problems. Reducing noise pollution involves determining its source, modifying machines to lessen sound levels, using protective equipment, and installing sound barriers when needed. Personal solutions include earplugs and noise-canceling headphones.
This slides tells about what are the consequences of noise;hearing loss and how it affects human being;protective devices for minimizing the risk level.
This document describes setting up network slicing on a Mininet testbed using FlowVisor. It outlines creating an upper and lower network slice managed by separate SDN controllers. Flow spaces are configured on switches to delegate traffic on certain ports to each slice. Connectivity tests show the slices isolate hosts from each other while intra-slice communication is maintained.
This document is a thesis submitted by Mohammed Abuibaid to Kocaeli University regarding adaptive beam-forming. It discusses various beam-forming techniques including switched array antennas, DSP-based phase manipulation, and beamforming by precoding. It also covers adaptive beamforming algorithms such as LMS, NLMS, RLS, and CM. Various beam patterns generated by these algorithms are presented. The document motivates the need for adaptive beamforming and 3D beamforming to improve energy efficiency in wireless networks.
Mobile Positioning System
This document discusses various methods for determining the location of mobile devices using cellular networks. It describes GPS, network-based methods like cell ID, timing advance, angle of arrival, and uplink time of arrival. It also covers MS-based methods like enhanced observed time difference and assisted GPS. The accuracy of each method is compared. Network-based methods are useful for applications like tracking stolen vehicles or providing emergency response, while more precise MS-based techniques allow for navigation systems. The document provides examples of how these positioning methods are used for emergency 911 location services.
Radio Access Network Functions
Radio Access Network Responsibilities
Antenna Configuration Requirements
RF Antenna Planning
Nominal Radio Plan For Kocaeli University
Introduction Videos about LTE AP Pro
Overview on LTE and 4.5 G Evolution Around the World
LTE Advance Pro: Enhancements
LTE Advance Pro: New Use Cases
Case Study: Turkey’s Mobile Operators Evolution towards 4.5 G
Summary of LTE Advance Pro
MATLAB Simulation: 2D Beamforming algorithms (LMS, NLMS RLS and CM)
References
Introduction to Convolutional Codes
Convolutional Encoder Structure
Convolutional Encoder Representation(Vector, Polynomial, State Diagram and Trellis Representations )
Maximum Likelihood Decoder
Viterbi Algorithm
MATLAB Simulation
Hard and Soft Decisions
Bit Error Rate Tradeoff
Consumed Time Tradeoff
This document summarizes key propagation models including Okumura, Hata, and COST231 models. It describes the models' parameters and equations. The Okumura model is empirical and based on extensive measurements in Japan. It accounts for factors like frequency, distance, and antenna heights. The Hata and COST231 models extend Okumura's validity to other frequencies and environments through curve-fitting. The document also explains how to extract data from the models' graphs using a web tool and simulate the models in MATLAB.
This document summarizes Carrier Sense Multiple Access (CSMA) techniques for digital data communication systems. It describes four CSMA access modes: 1-Persistent, Non-Persistent, P-Persistent, and O-Persistent. It also discusses CSMA protocol modifications like CSMA with Collision Detection (CSMA/CD), CSMA with Collision Avoidance (CSMA/CA), and Virtual Time CSMA (VTCSMA). Applications of different CSMA techniques are provided. At the end, it mentions including a MATLAB code sample for CSMA/CD.
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Optimizing Gradle Builds - Gradle DPE Tour Berlin 2024Sinan KOZAK
Sinan from the Delivery Hero mobile infrastructure engineering team shares a deep dive into performance acceleration with Gradle build cache optimizations. Sinan shares their journey into solving complex build-cache problems that affect Gradle builds. By understanding the challenges and solutions found in our journey, we aim to demonstrate the possibilities for faster builds. The case study reveals how overlapping outputs and cache misconfigurations led to significant increases in build times, especially as the project scaled up with numerous modules using Paparazzi tests. The journey from diagnosing to defeating cache issues offers invaluable lessons on maintaining cache integrity without sacrificing functionality.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
3. Hearing Loss
Exposure to high noise levels causes hearing loss. This
loss of hearing may be temporary, permanent, or a
combination of both.
Temporary hearing loss results from exposure to short-
term loud noises. As time passes, temporary hearing loss
will disappear. Permanent hearing losses cannot be
treated.
4. Speech Interference & masking :
One cannot effectively use speech communication in an
environment in which the background noise level is too high.
Sometimes, the masking of warning shouts by background
noise is responsible for industrial accidents.
6. Impulse Noise
Usually caused by Electromagnetic Interference, Scratches on the
recording disks, and ill synchronization in digital recording and
communication.
High levels of such a noise (+200 Decibels) may damage internal
organs, while (+180 Decibels) are enough to destroy or damage
human ears.
Impulsive sounds, such as Gun Shots, Hammer Blows, Explosions
of fireworks or other Blasts, are sounds that significantly exceed
the background sound pressure level for a very short duration.
9. To the human ear, Brown noise is similar to White noise but at a lower freq.
Examples in nature include waves on the beach and some wind noise.
Mimics the signal noise produced by Brownian motion.
Also another name for Red noise.
10. Similar to white noise, but has been filtered to make the sound
level appear constant at all frequencies to the human ear.
11. Contains an equal sound pressure level in each octave band.
Energy decreases as frequency increases.
12. Contains more energy as the frequency increases.
Similar to Brown noise except that the power density increases 6 dB per
octave as the frequency increases.
It is also known as Violet noise or differentiated white noise.
13. Green Noise : An unofficial term which can mean the mid-
frequencies of white noise, or the "background noise of the world".
Orange Noise : An unofficial term describing noise which has been
stripped of harmonious frequencies.
These bands of zero energy are centered
about the frequencies of musical notes. Since
all in-tune musical notes are eliminated, the
remaining spectrum could be said to consist
of Sour, Citrus, or “Orange" notes.
15. In other words, the signal contains equal power within a fixed
bandwidth at any center frequency.
Black Noise : A term with numerous conflicting definitions, but
most commonly refers to silence with occasional spikes.
White noise draws its name from white light in which the power
spectral density of the light is distributed over the visible band in
such a way that the eye's three color receptors (cone cells) are
approximately equally stimulated.
16.
17.
18. White noise has a variety of benefits and applications. Because
of its sound masking properties, white noise is ideal both as a
concentration aid and a relaxation aid, and is useful in a huge
range of situations.
Sleeping and Snoring Relief
Unlike other snoring solutions, white noise doesn't work by
trying to stop snoring.
Block snoring and other unwanted sounds, leading to deeper,
more restful sleep.
For traffic noise, or noisy neighbors, white noise offers the
sleeping solution.
19. White Noise Benefits and Uses
Office White Noise
White noise is used in the workplace, because real white
noise allows the brain to ignore even distracting ambient
sounds.
White noise CDs can be played in any radio or even through
a computer's speakers, to provide personal relief from
distractions.
Today, workplaces are adopting integrated white noise
systems, in which white noise is played evenly over a
speaker-system throughout large workspaces to provide
relief, and even some degree of speech privacy, to employees
21. White Noise Benefits and Uses
Travel White Noise & Portable white noise
Think of : Noisy Hotel Rooms, Plane rides.
Portable white noise can be taken on a plane to soothe the
chatter and crying babies.
White noise machines often have adapters available to make
them helpful on international trips -- and a white noise CD can
be taken anywhere a portable cd player can go.
22. White Noise Benefits and Uses
White Noise For Infants
Often there is a sudden sound : a slamming door, music, a
raised voice, or the TV , that disturbs a sleeping baby.
White noise for baby is a very popular solution for babies and
young children.
Perfectly safe, white noise for infants is designed to block
harsh and unfriendly noises.
In this way, white noise can be used to help create a safe and
comforting environment for infants.
There are special varieties of white noise ("baby white noise")
that featuring soothing sounds.
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25. The addition of natural or artificial sound ( such as or pink
noise) into an environment to cover up unwanted sound by
using auditory masking.
Mechanisms of masking is
Suppression.
This happens because the
original neural activity
caused by the first signal is
reduced by the neural
activity of the other sound.
26.
27. In air-to ground communications
between a pilot and the control
tower. Since there is often a large
amount of engine and wind noise
within the cockpit of the fighter
aircraft, communication is often a
difficult problem.
However, If a secondary microphone
is placed within the cockpit of an
aircraft, then one may estimate the
noise that is transmitted and when
the pilot speaks into the microphone, and subtract this estimate from
the transmitted signal ,thereby increasing the signal to noise ratio.
28.
29. d(n) : Desired Voice
V1(n) : Noise will be added to the desired Voice
V2(n) : Noise measured by a secondary sensor used to estimate V1(n)
X(n) = d(n) + V1(n) : Noisy corrupted Voice
V1
^
(n) : Estimated Noise using wiener filter
𝑑^(n) = X(n) - V1
^
(n) : Estimated Desired Voice
30. 𝑅v2 : Autocorrelation matrix of v2(n).
𝑟v1v2 : Vector of cross-correlation between the Noise V1(n) and
wiener filter input , V2(n) .
𝑟v1v2(k) = E{ V1(n) V2(n-k)}
= E{ [ x(n) – d(n) ] V2(n – k ) }
= E{X(n) V2(n – k) } – E { d(n) V2(n –k ) }
If we assume that v2(n) is uncorrelated with d(n) ,then the second
term is zero and the cross-correlation becomes
𝑟v1v2(k)= E{x(n) V2(n –k)}= 𝑟xv2(k)
There for the Wiener- Hopf equation is