... designed to widen participation in post-compulsory learning and notions of socialinclusion Whilst both widening participation andsocialinclusion can be viewed as distinct policy areas, the focus ... discourse of socialinclusion contained within the New Labour project is both partial and conditional and, as a result of this, '…if the government makes a reasonable offer of a route into social inclusion, ... colleges and centres for adult and community education, and as a student studying social policy These two roles of practitioner and student have interacted in a number of ways I chose to study social...
... mirror those of Heidegger and Marcuse’s, while extending Manovich’s understanding of new media to social media Social media is a product of both cybernetics and modern science and thus reproduces certain ... misunderstanding” and extending knowledge about the world (ibid) Both the radio and television were expected to radically transform politics and usher in a new era of public participation and create ... concerned with efficiency and effectiveness, and thus seeks to control and optimize systems in order to accurately predict and manipulate outcomes The well-known Shannon and Weaver model of communication,...
... broadcasting, and advertising It is also a reflection of its speakers’ identity, economic andsocial class In fact, language is itself a tool or passport into a particular identity, economic andsocial ... between the poor and rich, formal and non formal education and help people cope with political, economic andsocial changes adult education programs need to respond to language conflicts and inequalities ... language to influence and persuade those who have power and also because their languages, experiences and knowledge are down played in the new policies and technologies Conclusion and recommendations...
... an insightful and passionate reviewer of the working papers, and an unwavering advocate for social justice and the socialinclusion of all people Social Inclusion, Anti-Racism and Democratic ... Donnelly and Jay Coakley — The Role of Recreation in Promoting SocialInclusion Andrew Mitchell and Richard Shillington — Poverty, Inequality, andSocialInclusion Catherine Frazee — Thumbs Up! Inclusion, ... meaningful and constructive discussion of socialinclusion Thus for socialinclusion to matter, for it to resonate, it must provide space for a discussion of oppression and discrimination Social inclusion...
... successful socialinclusion at school (McDougall, DeWitt, King, Miller, & Killip, 2004) and are often the cause behind students with special needs feeling socially isolated Children bring a set of social ... of inclusion for children with special needs is to develop social acceptance and increase positive social interactions with typical peers, research is needed to identify effective skills and ... students in developing natural and ongoing social relationships; c) Implementing an array of strategies to develop students pro -social skills; and d) structuring the classroom and instruction to allow...
... University of Social Sciences and Humanities, Vietnam National University, although English has long been introduced as a compulsory subject, the teaching and learning of ESP in general and reading ... teaching and learning reading and relationship between this skill and the other language skills Part C is the main part of the study It consists of two chapters − Chapter III aims at identifying and ... strong points and weak points of the teaching and learning of reading ESP in Department of Linguistics and Vietnamese Studies at USSH – VNU based on the two survey questionnaires and the observation...
... Hz to 80 Hz; band is band-pass and covers 80 Hz to kHz; band is high-pass and covers above kHz; and band is also high-pass and covers above kHz At the encoder the gain of each band is adaptively ... number of bands and the pre-emphasis strategy that that they employ Dolby A, developed for professional use, divides the signal spectrum into four frequency bands: band is low-pass and covers ... terms and in terms of probability functions Bayesian inference theory provides a generalised framework for statistical processing of random signals, and for formulating and solving estimation and...
... wideband noise whose spectrum has a non-flat shape; examples are pink noise, brown noise and autoregressive noise (e) Impulsive noise: consists of short-duration pulses of random amplitude and random ... sensation and quality Figures 2.9(a) and (b) show examples of the spectra of car noise recorded from a BMW and a Volvo respectively The noise in a car is nonstationary, and varied, and may include ... the signal and the noise processes The models are then used for the decoding of the underlying states of the signal and noise, and for noisy signal recognition and enhancement Noise and Distortion...
... of deterministic signals, random signals and random processes A random process generates random signals, and the collection of all signals that can be generated by a random process is the space ... that transforms a random input process X into an output process Y The input and output signals x(m) and y(m) are realisations of the random processes X and Y respectively If x(m) and y(m) are both ... Autocorrelation and power spectrum of impulsive noise Impulsive noise is a random, binary-state (“on/off”) sequence of impulses of random amplitudes and random time of occurrence In Chapter 12, a random...
... a symmetric and an asymmetric pdf and their respective mode, mean and median and the relations to MAP, MAVE and MMSE estimates 4.2.6 The Influence of the Prior on Estimation Bias and Variance ... is the random input of the AR model and n is the random noise Using Equation (4.3), the signal restoration process involves the estimation of both the model parameter vector θ and the random input ... vector µx and covariance matrix Σxx, and that the noise n(m) is also Gaussian with mean vector µn and covariance matrix Σnn The signal and noise pdfs model the prior spaces of the signal and the...
... 5.8(a) and 5.8(b) illustrate a 4-state HMM and its state–time diagram Since the number of states and the state parameters of an HMM are time-invariant, a state-time diagram is a repetitive and regular ... subtraction methods described in Chapters and 11) or by combining the noise and the signal models to model the noisy Signal and Noise Model Combination and Decomposition 171 signal The model combination ... each cluster is described by the centroid vector and the cluster probability, and the state observation model consists of M cluster centroids and the associated pmf {µik, Pik; i=1, , N, k=1,...
... Filter for Additive Noise Reduction Consider a signal x(m) observed in a broadband additive noise n(m)., and model as y(m) = x(m) + n(m) (6.45) Assuming that the signal and the noise are uncorrelated, ... x(m) and the noise n(m): Ryy = Rxx + Rnn (6.46) rxy = rxx (6.47) and we can also write where Ryy, Rxx and Rnn are the autocorrelation matrices of the noisy signal, the noise-free signal and the ... ) (6.50) where PXX(f) and PNN(f) are the signal and noise power spectra Dividing the numerator and the denominator of Equation (6.50) by the noise power spectra PNN(f) and substituting the variable...
... dynamics of the signal process and an observation equation models the noisy observation signal For a signal x(m) and noisy observation y(m), the state equation model and the observation model are ... m | m − 1) ~ ( m| m − 1)] = x (7.20) and we have also used the assumption that the signal and the noise are uncorrelated Substitution of Equations (7.9) and (7.16) in Equation (7.15) yields the ... observed in a random noise The state and observation equations for this problem are given by x(m)= x(m − 1) = x y(m) = x + n(m) (7.41) (7.42) Note that Φ(m,m–1)=1, state excitation e(m)=0 and H(m)=1...
... X Ba ) 2 (8.55) where X and x are the signal matrix and vector defined by Equations (8.12) and (8.13), and similarly XB and xB are the signal matrix and vector for the backward predictor ... flexibility and better performance Sub-Band Linear Prediction Model 253 In sub-band linear prediction, the signal x(m) is passed through a bank of N band-pass filters, and is split into N sub-band signals ... of a sub-band linear prediction model Linear Prediction Models 254 where Bk and fk0 are the bandwidth and the centre frequency of the kth subband respectively To ensure that each sub-band LP parameters...
... Hz to 80 Hz; band is band-pass and covers 80 Hz to kHz; band is high-pass and covers above kHz; and band is also high-pass and covers above kHz At the encoder the gain of each band is adaptively ... number of bands and the pre-emphasis strategy that that they employ Dolby A, developed for professional use, divides the signal spectrum into four frequency bands: band is low-pass and covers ... each band if the signal level falls 45 dB below the maximum recording level The Dolby B and Dolby C systems are designed for consumer audio systems, and use two bands instead of the four bands...
... terms and in terms of probability functions Bayesian inference theory provides a generalised framework for statistical processing of random signals, and for formulating and solving estimation and ... 4.1.1 Dynamic and Probability Models in Estimation 91 4.1.2 Parameter Space and Signal Space 92 4.1.3 Parameter Estimation and Signal Restoration 93 4.1.4 Performance Measures and Desirable ... incomplete and noisy Hence, noise reductionand the removal of channel distortion is an important part of a signal processing system The aim of this book is to provide a coherent and structured...
... cases of a sine wave and a purely random signal For a periodic signal, the power is concentrated in extremely narrow bands of frequencies, indicating the existence of structure and the predictable ... frequencies, such as the bass, without affecting other frequencies, and in subband coding different frequency bands are coded independently and allocated different numbers of bits (ii) Sinusoidal functions ... decision making and estimation problems, and in systems analysis x(t) PXX(f) f t (a) PXX(f) x(t) t (b) Figure 9.1 The concentration/spread of power in frequency indicates the correlated or random character...
... case of a band-limited random signal if the sampling rate is greater than M times the Nyquist rate However, in many practical cases, the signal is a realisation of a random process, and the sampling ... recognition and decision making systems We started this chapter with a study of the ideal interpolation of a band-limited signal, and its applications in digital-to-analog conversion and in multirate ... original base-band spectrum X(f) and the repetitions or images of X(f) spaced uniformly at frequency intervals of Fs=1/Ts When the sampling frequency is above the Nyquist rate, the baseband spectrum...
... a signal in the time and the frequency domains 335 Spectral Subtraction where y(m), x(m) and n(m) are the signal, the additive noise and the noisy signal respectively, and m is the discrete ... White Noise IEEE Trans Acoustics, Speech and Signal Processing, ASSP-26, 5, pp 471–472 LINHARD K and KLEMM H (1997) Noise Reduction with Spectral Subtraction and Median Filtering for Suppression ... )+ N ( f ) (11.2) where Y(f), X(f) and N(f) are the Fourier transforms of the noisy signal y(m), the original signal x(m) and the noise n(m) respectively, and f is the frequency variable In spectral...
... amplitude-modulated binary-state sequence, and expressed as ni (m) = n(m) b(m) (12.6) where b(m) is a binary-state random sequence of ones and zeros, and n(m) is a random noise process Assuming that impulsive ... Noise 12.1.1 Autocorrelation and Power Spectrum of Impulsive Noise Impulsive noise is a non-stationary, binary-state sequence of impulses with random amplitudes and random positions of occurrence ... of short duration pulses of a random amplitude, duration, and time of occurrence, and may be modelled as the output of a filter excited by an amplitude-modulated random binary sequence as P −1...