Health effects from noise
Noise health effects are the physical and psychological health consequences of regular exposure to consistent elevated sound levels. Elevated workplace or environmental noise can cause hearing impairment, hypertension, ischemic heart disease and sleep disturbance. Changes in the immune system and birth defects have been attributed to noise exposure. Although age-related health effects occur with age, in many countries the cumulative impact of noise is sufficient to impair the hearing of a large fraction of the population over the course of a lifetime. Noise exposure has been known to induce tinnitus, hypertension and other cardiovascular adverse effects. Chronic noise exposure has been associated with sleep disturbances and increased incidence of diabetes. Adverse cardiovascular effects occur from chronic exposure to noise due to the sympathetic nervous system's inability to habituate; the sympathetic nervous system maintains lighter stages of sleep when the body is exposed to noise, which does not allow blood pressure to follow the normal rise and fall cycle of an undisturbed circadian rhythm.
Stress from time spent around elevated noise levels has been linked with increased workplace accident rates and aggression and other anti-social behaviors. The most significant sources are vehicles, prolonged exposure to loud music, industrial noise. There are an 10,000 deaths per year as a result of noise in the European Union. Noise-induced hearing loss is a permanent shift in pure-tone thresholds, resulting in sensorineural hearing loss; the severity of a threshold shift is dependent on severity of noise exposure. Noise-induced threshold shifts are seen as a notch on an audiogram from 3000–6000 Hz, but most at 4000 Hz. Exposure to loud noises, either in a single traumatic experience or over time, can damage the auditory system and result in hearing loss and sometimes tinnitus as well. Traumatic noise exposure can happen at work, at play, and/or by accident Noise induced hearing loss is sometimes unilateral and causes patients to lose hearing around the frequency of the triggering sound trauma.
Tinnitus is an auditory disorder characterized by the perception of a sound in the ear in the absence of an external sound source. There are two types of tinnitus: objective. Subjective can only be heard "in the head" by the person affected. Objective tinnitus can be heard from those around the affected person. Though the pathophysiology of tinnitus isn't known, noise exposure can be a contributing factor. Noise-induced tinnitus can be temporary or permanent depending on the type and amount of noise a person was exposed to. Noise has been associated with important cardiovascular health problems hypertension. Noise levels of 50 dB at night may increase the risk of myocardial infarction by chronically elevating cortisol production. Roadway noise levels are sufficient to constrict arterial blood flow and lead to elevated blood pressure. Vasoconstriction can result through medical stress reactions. Causal relationships have been discovered between noise and psychological effects such as annoyance, psychiatric disorders, effects on psychosocial well-being.
Exposure to intense levels of noise can cause violent reactions. Noise has been shown to be a factor that attributed to violent reactions; the psychological impacts of noise include an addiction to loud music. This was researched in a study where non-professional musicians were found to have loudness addictions more than non-musician control subjects. Psychological health effects from noise include anxiety. Individuals who have hearing loss, including noise induced hearing loss, may have their symptoms alleviated with the use of hearing aids. Individuals who do not seek treatment for their loss are 50% more to have depression than their aided peers; these psychological effects can lead to detriments in physical care in the form of reduced self-care, work-tolerance, increased isolation. Auditory stimuli can serve as psychological triggers for individuals with post traumatic stress disorder. Research commissioned by Rockwool, a multi-national insulation manufacturer headquartered in Denmark, reveals that in the UK one third of victims of domestic disturbances claim loud parties have left them unable to sleep or made them stressed in the last two years.
Around one in eleven of those affected by domestic disturbances claims it has left them continually disturbed and stressed. More than 1.8 million people claim noisy neighbours have made their life a misery and they cannot enjoy their own homes. The impact of noise on health is a significant problem across the UK given that more than 17.5 million Britons have been disturbed by the inhabitants of neighbouring properties in the last two year. For one in ten Britons this is a regular occurrence; the extent of the problem of noise pollution for public health is reinforced by figures collated by Rockwool from local authority responses to a Freedom of Information Act request. This research reveals in the period April 2008 - 2009 UK councils received 315,838 complaints about noise pollution from private residences; this resulted in environmental health officers across the UK serving 8,069 noise abatement notices, or citations under the terms of the Anti-Social Behaviour Act. Westminster City Council has received more complaints per head of population than any other district in the UK with 9,814 grievances about noise, which equates to 42.32 c
Value noise is a type of noise used as a procedural texture primitive in computer graphics. It is conceptually different from, confused with gradient noise, examples of which are Perlin noise and Simplex noise; this method consists of the creation of a lattice of points. The noise function returns the interpolated number based on the values of the surrounding lattice points. For many applications, multiple octaves of this noise can be generated and summed together, just as can be done with Perlin noise and Simplex noise, in order to create a form of fractal noise. Elias, Hugo. "Perlin noise". Freespace.virgin.net. Archived from the original on 2008-07-24. - an explanation and implementation of Value Noise, mislabeled as Perlin noise. Lesson explaining in a simple way how Value Noise works
Radio astronomy is a subfield of astronomy that studies celestial objects at radio frequencies. The first detection of radio waves from an astronomical object was in 1932, when Karl Jansky at Bell Telephone Laboratories observed radiation coming from the Milky Way. Subsequent observations have identified a number of different sources of radio emission; these include stars and galaxies, as well as new classes of objects, such as radio galaxies, quasars and masers. The discovery of the cosmic microwave background radiation, regarded as evidence for the Big Bang theory, was made through radio astronomy. Radio astronomy is conducted using large radio antennas referred to as radio telescopes, that are either used singularly, or with multiple linked telescopes utilizing the techniques of radio interferometry and aperture synthesis; the use of interferometry allows radio astronomy to achieve high angular resolution, as the resolving power of an interferometer is set by the distance between its components, rather than the size of its components.
Before Jansky observed the Milky Way in the 1930s, physicists speculated that radio waves could be observed from astronomical sources. In the 1860s, James Clerk Maxwell's equations had shown that electromagnetic radiation is associated with electricity and magnetism, could exist at any wavelength. Several attempts were made to detect radio emission from the Sun including an experiment by German astrophysicists Johannes Wilsing and Julius Scheiner in 1896 and a centimeter wave radiation apparatus set up by Oliver Lodge between 1897 and 1900; these attempts were unable to detect any emission due to technical limitations of the instruments. The discovery of the radio reflecting ionosphere in 1902, led physicists to conclude that the layer would bounce any astronomical radio transmission back into space, making them undetectable. Karl Jansky made the discovery of the first astronomical radio source serendipitously in the early 1930s; as an engineer with Bell Telephone Laboratories, he was investigating static that interfered with short wave transatlantic voice transmissions.
Using a large directional antenna, Jansky noticed that his analog pen-and-paper recording system kept recording a repeating signal of unknown origin. Since the signal peaked about every 24 hours, Jansky suspected the source of the interference was the Sun crossing the view of his directional antenna. Continued analysis showed that the source was not following the 24-hour daily cycle of the Sun but instead repeating on a cycle of 23 hours and 56 minutes. Jansky discussed the puzzling phenomena with his friend and teacher Albert Melvin Skellett, who pointed out that the time between the signal peaks was the exact length of a sidereal day. By comparing his observations with optical astronomical maps, Jansky concluded that the radiation source peaked when his antenna was aimed at the densest part of the Milky Way in the constellation of Sagittarius, he concluded that since the Sun were not large emitters of radio noise, the strange radio interference may be generated by interstellar gas and dust in the galaxy.
Jansky announced his discovery in 1933. He wanted to investigate the radio waves from the Milky Way in further detail, but Bell Labs reassigned him to another project, so he did no further work in the field of astronomy, his pioneering efforts in the field of radio astronomy have been recognized by the naming of the fundamental unit of flux density, the jansky, after him. Grote Reber was inspired by Jansky's work, built a parabolic radio telescope 9m in diameter in his backyard in 1937, he began by repeating Jansky's observations, conducted the first sky survey in the radio frequencies. On February 27, 1942, James Stanley Hey, a British Army research officer, made the first detection of radio waves emitted by the Sun; that year George Clark Southworth, at Bell Labs like Jansky detected radiowaves from the sun. Both researchers were bound by wartime security surrounding radar, so Reber, not, published his 1944 findings first. Several other people independently discovered solar radiowaves, including E. Schott in Denmark and Elizabeth Alexander working on Norfolk Island.
At Cambridge University, where ionospheric research had taken place during World War II, J. A. Ratcliffe along with other members of the Telecommunications Research Establishment that had carried out wartime research into radar, created a radiophysics group at the university where radio wave emissions from the Sun were observed and studied; this early research soon branched out into the observation of other celestial radio sources and interferometry techniques were pioneered to isolate the angular source of the detected emissions. Martin Ryle and Antony Hewish at the Cavendish Astrophysics Group developed the technique of Earth-rotation aperture synthesis; the radio astronomy group in Cambridge went on to found the Mullard Radio Astronomy Observatory near Cambridge in the 1950s. During the late 1960s and early 1970s, as computers became capable of handling the computationally intensive Fourier transform inversions required, they used aperture synthesis to create a'One-Mile' and a'5 km' effective
Quantization (signal processing)
Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set to output values in a smaller set with a finite number of elements. Rounding and truncation are typical examples of quantization processes. Quantization is involved to some degree in nearly all digital signal processing, as the process of representing a signal in digital form ordinarily involves rounding. Quantization forms the core of all lossy compression algorithms; the difference between an input value and its quantized value is referred to as quantization error. A device or algorithmic function that performs quantization is called a quantizer. An analog-to-digital converter is an example of a quantizer; because quantization is a many-to-few mapping, it is an inherently non-linear and irreversible process. The set of possible input values may be infinitely large, may be continuous and therefore uncountable; the set of possible output values may be countably infinite. The input and output sets involved in quantization can be defined in a rather general way.
For example, vector quantization is the application of quantization to multi-dimensional input data. An analog-to-digital converter can be modeled as two processes: quantization. Sampling converts a time-varying voltage signal into a discrete-time signal, a sequence of real numbers. Quantization replaces each real number with an approximation from a finite set of discrete values. Most these discrete values are represented as fixed-point words. Though any number of quantization levels is possible, common word-lengths are 8-bit, 16-bit and 24-bit. Quantizing a sequence of numbers produces a sequence of quantization errors, sometimes modeled as an additive random signal called quantization noise because of its stochastic behavior; the more levels a quantizer uses, the lower is its quantization noise power. Rate–distortion optimized quantization is encountered in source coding for lossy data compression algorithms, where the purpose is to manage distortion within the limits of the bit rate supported by a communication channel or storage medium.
The analysis of quantization in this context involves studying the amount of data, used to represent the output of the quantizer, studying the loss of precision, introduced by the quantization process. As an example, rounding a real number x to the nearest integer value forms a basic type of quantizer – a uniform one. A typical uniform quantizer with a quantization step size equal to some value Δ can be expressed as Q = Δ ⋅ ⌊ x Δ + 1 2 ⌋ = Δ ⋅ floor ,where the notation ⌊ ⌋ or floor depicts the floor function; the essential property of a quantizer is that it has a countable set of possible output values that has fewer members than the set of possible input values. The members of the set of output values may have integer, rational, or real values. For simple rounding to the nearest integer, the step size Δ is equal to 1. With Δ = 1 or with Δ equal to any other integer value, this quantizer has real-valued inputs and integer-valued outputs; when the quantization step size is small relative to the variation in the signal being quantized, it is simple to show that the mean squared error produced by such a rounding operation will be Δ 2 / 12.
Mean squared error is called the quantization noise power. Adding one bit to the quantizer halves the value of Δ, which reduces the noise power by the factor ¼. In terms of decibels, the noise power change is 10 ⋅ log 10 ≈ − 6 d B; because the set of possible output values of a quantizer is countable, any quantizer can be decomposed into two distinct stages, which can be referred to as the classification stage and the reconstruction stage, where the classification stage maps the input value to an integer quantization index k and the reconstruction stage maps the index k to the reconstruction value y k, the output approx
Burst noise is a type of electronic noise that occurs in semiconductors and ultra-thin gate oxide films. It is called random telegraph noise, popcorn noise, impulse noise, bi-stable noise, or random telegraph signal noise, it consists of sudden step-like transitions between two or more discrete voltage or current levels, as high as several hundred microvolts, at random and unpredictable times. Each shift in offset voltage or current lasts from several milliseconds to seconds, sounds like popcorn popping if hooked up to an audio speaker. Popcorn noise was first observed in early point contact diodes re-discovered during the commercialization of one of the first semiconductor op-amps. No single source of popcorn noise is theorized to explain all occurrences, however the most invoked cause is the random trapping and release of charge carriers at thin film interfaces or at defect sites in bulk semiconductor crystal. In cases where these charges have a significant impact on transistor performance, the output signal can be substantial.
These defects can be caused by manufacturing processes, such as heavy ion implantation, or by unintentional side-effects such as surface contamination. Individual op-amps can be screened for popcorn noise with peak detector circuits, to minimize the amount of noise in a specific application. Burst noise is modeled mathematically by means of the telegraph process, a Markovian continuous-time stochastic process that jumps discontinuously between two distinct values. Atomic electron transition Telegraph process A review of popcorn noise and smart filtering, www.advsolned.com
Atmospheric noise is radio noise caused by natural atmospheric processes lightning discharges in thunderstorms. On a worldwide scale, there are about 40 lightning flashes per second – ≈3.5 million lightning discharges per day. In 1925, AT&T Bell Laboratories started investigating the sources of noise in its transatlantic radio telephone service. Karl Jansky, a 22-year-old researcher, undertook the task. By 1930, a radio antenna for a wavelength of 14.6 meters was constructed in Holmdel, NJ, to measure the noise in all directions. Jansky recognized three sources of radio noise; the first source was local thunderstorms. The second source was weaker noise from more distant thunderstorms; the third source was a still weaker hiss that turned out to be galactic noise from the center of the Milky Way. Jansky's research made him the father of radio astronomy. Atmospheric noise is radio noise caused by natural atmospheric processes lightning discharges in thunderstorms, it is caused by cloud-to-ground flashes as the current is much stronger than that of cloud-to-cloud flashes.
On a worldwide scale, 3.5 million lightning flashes occur daily. This is about 40 lightning flashes per second; the sum of all these lightning flashes results in atmospheric noise. It can be observed, with a radio receiver, in the form of a combination of white noise and impulse noise; the power-sum varies with nearness of thunderstorm centers. Although lightning has a broad-spectrum emission, its noise power increases with decreasing frequency. Therefore, at low frequency and low frequency, atmospheric noise dominates, while at high frequency, man-made noise dominates in urban areas. From 1960s to 1980s, a worldwide effort was made to measure variations. Results have been documented in CCIR Report 322. CCIR 322 provided seasonal world maps showing the expected values of the atmospheric noise figure Fa at 1 MHz during four hour blocks of the day. Another set of charts relates the Fa at 1 MHz to other frequencies. CCIR Report 322 has been superseded by ITU P.372 publication. Atmospheric noise and variation is used to generate high quality random numbers.
Random numbers have interesting applications in the security domain. Radio atmospheric Singh, Big Bang: The Origin of the Universe, Harper Perennial, ISBN 978-0-00-716221-5 Spaulding, Arthur D..