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Audio takes up much less space than video, but uncompressed audio coupled with compressed video uses up a large percentage of the available bandwidth Compressing the audio can result in a small loss of quality, but if the resulting space is used instead for video, it may improve the video quality significantly In essence, reducing both the audio and the video is more effective Usually video is compressed more than audio, since the ear is more sensitive to detail loss than the eye Just as MPEG compression takes advantage of characteristics of the human eye, modern audio compression relies on detailed understanding of the human ear This is called psychoacoustic or perceptual coding Picture again your telephone conversation with a friend Imagine that your friend lives near an airport, so that when a plane takes off, your friend cannot hear you over the sound of the airplane In a situation like this, you quickly learn to stop talking when a plane is taking off, since your friend will not hear you The airplane has masked the sound of your voice At the opposite end of the loudness spectrum from airplane noise is background noise, such as a ticking clock While you are speaking, your friend cannot hear the clock, but if you stop, then the background noise is no longer masked The hairs in your inner ear are sensitive to sound pressure at different frequencies (pitches) When stimulated by a loud sound, they are incapable of sensing softer sounds at the same pitch Because the hairs for similar frequencies are near each other, a stimulated audio receptor nerve will interfere with nearby receptors and cause them to be less sensitive This is called frequency masking Human hearing ranges roughly from low frequencies of 20 Hz to high frequencies of 20,000 Hz (20 kHz) The ear is most sensitive to the frequency range from about 2 to 5 kHz, which corresponds to the range of the human voice Because aural sensitivity varies in a nonlinear fashion, sounds at some frequencies mask more neighboring sounds than at other frequencies Experiments have established certain critical bands of varying size that correspond to the masking function of human hearing (Figure 36) Another characteristic of the human audio sensory system is that sounds cannot be sensed when they fall below a certain loudness (or amplitude) This sensitivity threshold, as with everything else, is not linear In other words, the threshold is at louder or softer points at different frequencies The overall threshold varies a little from person to person some people have better hearing than others The threshold of hearing is adaptive; the. java upc-a Generate UPC-A barcode in Java class using Java UPC-A ...
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The configuration for the default implementation is: <barcode> < upc-a > <height>{ length:15mm}</height> <module-width>{length:0.33mm}</module-width> ... In many practical learning settings, only a subset of the relevant instance features might be observable For example, in training or using the Bayesian belief network of Figure 63, we might have data where only a subset of the network variables Storm, Lightning, Thunder, ForestFire, Campfire, and BusTourGroup have been observed Many approaches have been proposed to handle the problem of learning in the presence of unobserved variables As we saw in 3, if some variable is sometimes observed and sometimes not, then we can use the cases for which it has been observed to learn to predict its values when it is not In this section we describe the EM algorithm (Dempster et al 1977), a widely used approach to learning in the presence of unobserved variables The EM algorithm can be used even for variables whose value is never directly observed, provided the general form of the probability distribution governing these variables is known The EM algorithm has been used to train Bayesian belief networks (see Heckerman 1995) as well as radial basis function networks discussed in Section 84 The EM algorithm is also the basis for many unsupervised clustering algorithms (eg, Cheeseman et al 1988), and it is the basis for the widely used Baum-Welch forward-backward algorithm for learning Partially Observable Markov Models (Rabiner 1989) java upc-a Java UPC-A Generator | Barcode UPCA Generation in Java Class ...
UPC-A is also known as Universal Product Code version A, UPC-A Supplement 5/Five-digit Add-On, UPC-A Supplement 2/Two-digit Add-On, UPC-A +5, ... java upc-a Generate and draw UPC-A for Java
Integrate UPC-A barcode generation function to Java applications for drawing UPC-A in Java . [Session started at 2009-01-17 10:00:55 -0500] 2009-01-17 10:00:59432 AlertsProgress[861:20b] buttons index: 0 2009-01-17 10:01:01936 AlertsProgress[861:20b] buttons index: 1 2009-01-17 10:01:04336 AlertsProgress[861:20b] buttons index: 2 ear can adjust its sensitivity in order to pick up soft sounds when not overloaded by loud sounds This characteristic causes the effect of temporal masking, in which you are unable to hear soft sounds for up to 200 milliseconds after a loud sound and for 2 or 3 milliseconds before a loud sound9 Figure 11-19 java upc-a racca3141/UPC: Build a UPC-A label. - GitHub
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Java UPC-A Barcodes Generator for Java, J2EE, JasperReports - Download as PDF File (.pdf), Text File (.txt) or read online. The easiest way to introduce the EM algorithm is via an example Consider a problem in which the data D is a set of instances generated by a probability distribution that is a mixture of k distinct Normal distributions This problem setting is illustrated in Figure 64 for the case where k = 2 and where the instances are the points shown along the x axis Each instance is generated using a two-step process First, one of the k Normal distributions is selected at random Second, a single random instance xi is generated according to this selected distribution This process is repeated to generate a set of data points as shown in the figure To simplify our discussion, we consider the special case where the selection of the single Normal distribution at each step is based on choosing each with uniform probability, where each of the k Normal distributions has the same variance a2,and where a2 is known The learning task is to output a hypothesis h = (FI, pk) that describes the means of each of the k distributions We would like to find DVD uses three audio data reduction systems: Dolby Digital (AC-3) coding, MPEG audio coding, and DTS (Coherent Acoustics) coding All use mathematical models of human hearing based on sensitivity thresholds, frequency masking, and temporal masking to remove sounds that you cannot hear The resulting information is compressed to about one-third to onetwelfth the original size with little to no perceptible loss in quality (see Table 31) Digital audio is sampled by taking snapshots of an analog signal thousands of times a second Each sample is a number that represents the amplitude (strength) of the waveform at that instance in time Perceptual 11: FIGURE 64 Instances generated by a mixture of two Normal distributions with identical variance aThe instances are shown by the points along the x axis If the means of the Normal distributions are unknown, the EM algorithm can be used to search for their maximum likelihood estimates 9 How can masking work backward in time The signal presented by the ear to the brain is a composite built up from stimuli received over a period of about 200 milliseconds A loud noise effectively overrides a small portion of the earlier stimuli before it can be accumulated and sent to the brain Figure 11-20 3 java upc-a BE THE CODER > Barcodes > Barcode4j Examples > Barcode UPC-A
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UPC-A barcode generator can print UPC-A and saved it as GIF and JPEG images using Java class library. Generated UPC-A barcode images can be displayed ...
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