Advantages And Disadvantages Of Smart Antenna

Advantages And Disadvantages Of Smart Antenna The Direction of Arrival (DOA) estimation algorithm which may take various forms generally follows from the homogeneous solution of the wave equation. The models of interest in this dissertation may equally apply to an EM wave as well as to an acoustic wave. Assuming that the propagation model is fundamentally the same, we will, for analytical expediency, show that it can follow from the solution of Maxwells equations, which clearly are only valid for EM waves. In empty space the equation can be written as: =0 (3.1) =0 (3.2) (3.3) (3.4) where . and ÃÆ'-, respectively, denote the divergence and curl. Furthermore, B is the magnetic induction. E denotes the electric field, whereas and are the magnetic and dielectric constants respectively. Invoking 3.1 the following curl property results as: (3.5) (3.6) (3.7) The constant c is generally referred to as the speed of propagation. For EM waves in free space, it follows from the derivation c = 1 / = 3 x m / s. The homogeneous wave equation (3.7) constitutes the physical motivation for our assumed data model, regardless of the type of wave or medium. In some applications, the underlying physics are irrelevant, and it is merely the mathematical structure of the data model that counts. 3.2 Plane wave In the physics of wave propagation, a plane wave is a constant-frequency wave whose wave fronts are infinite parallel planes of constant peak-to-peak amplitude normal to the phase velocity vector[]. Actually, it is impossible to have a rare plane wave in practice, and only a plane wave of infinite extent can propagate as a plane wave. Actually, many waves are approximately regarded as plane waves in a localized region of space, e.g., a localized source such as an antenna produces a field which is approximately a plane wave far enough from the antenna in its far-field region. Likely, we can treat the waves as light rays which correspond locally to plane waves, when the length scales are much longer than the waves wavelength, as is often appearing of light in the field of optics. 3.2.1 Mathematical definition Two functions which meet the criteria of having a constant frequency and constant amplitude are defined as the sine or cosine functions. One of the simplest ways to use such a sinusoid involves defining it along the direction of the x axis. As the equation shown below, it uses the cosine function to express a plane wave travelling in the positive x direction. (3.8) Where A(x,t) is the magnitude of the shown wave at a given point in space and time. is the amplitude of the wave which is the peak magnitude of the oscillation. k is the waves wave number or more specifically the angular wave number and equals 2à Ã¢â€š ¬/ÃŽÂ », where ÃŽÂ » is the wavelength of the wave. k has the units of radians per unit distance and is a standard of how rapidly the disturbance changes over a given distance at a particular point in time. x is a point along the x axis. y and z are not considered in the equation because the waves magnitude and phase are the same at every point on any given y-z plane. This equation defines what that magnitude and phase are. is the waves angular frequency which equals 2à Ã¢â€š ¬/T, and T is the period of the wave. In detail, omega, has the units of radians per unit time and is also a standard of how rapid the disturbance changing in a given length of time at a particular point in space. is a given particular point in time, and varphi , is the wave phase shift with the units of radians. It must make clear that a positive phase shift will shifts the wave along the negative x axis direction at a given point of time. A phase shift of 2à Ã¢â€š ¬ radians means shifting it one wavelength exactly. Other formulations which directly use the waves wavelength, period T, frequency f and velocity c, are shown as follows: A=A_o cos[2pi(x/lambda- t/T) + varphi], (3.9) A=A_o cos[2pi(x/lambda- ft) + varphi], (3.10) A=A_o cos[(2pi/lambda)(x- ct) + varphi], (3.11) To appreciate the equivalence of the above set of equations denote that f=1/T,! and c=lambda/T=omega/k,! 3.2.2 Application Plane waves are solutions for a scalar wave equation in the homogeneous medium. As for vector wave equations, e.g., waves in an elastic solid or the ones describing electromagnetic radiation, the solution for the homogeneous medium is similar. In vector wave equations, the scalar amplitude is replaced by a constant vector. e.g., in electromagnetism is the vector of the electric field, magnetic field, or vector potential. The transverse wave is a kind of wave in which the amplitude vector is perpendicular to k, which is the case for electromagnetic waves in an isotropic space. On the contrast, the longitudinal wave is a kind of wave in which the amplitude vector is parallel to k, typically, such as for acoustic waves in a gas or fluid. The plane wave equation is true for arbitrary combinations of à Ã¢â‚¬ ° and k. However, all real physical mediums will only allow such waves to propagate for these combinations of à Ã¢â‚¬ ° and k that satisfy the dispersion relation of the mediums. The dispersion relation is often demonstrated as a function, à Ã¢â‚¬ °(k), where ratio à Ã¢â‚¬ °/|k| gives the magnitude of the phase velocity and dà Ã¢â‚¬ °/dk denotes the group velocity. As for electromagnetism in an isotropic case with index of refraction coefficient n, the phase velocity is c/n, which equals the group velocity on condition that the index is frequency independent. In linear uniform case, a wave equation solution can be demonstrated as a superposition of plane waves. This method is known as the Angular Spectrum method. Actually, the solution form of the plane wave is the general consequence of translational symmetry. And in the more general case, for periodic structures with discrete translational symmetry, the solution takes the form of Bloch waves, which is most famous in crystalline atomic materials, in the photonic crystals and other periodic wave equations. 3.3 Propagation Many physical phenomena are either a result of waves propagating through a medium or exhibit a wave like physical manifestation. Though 3.7 is a vector equation, we only consider one of its components, say E(r,t) where r is the radius vector. It will later be assumed that the measured sensor outputs are proportional to E(r,t). Interestingly enough, any field of the form E(r,t) = , which satisfies 3.7, provided with T denoting transposition. Through its dependence on only, the solution can be interpreted as a wave traveling in the direction, with the speed of propagation. For the latter reason, ÃŽÂ ± is referred to as the slowness vector. The chief interest herein is in narrowband forcing functions. The details of generating such a forcing function can be found in the classic book by Jordan [59]. In complex notation [63] and taking the origin as a reference, a narrowband transmitted waveform can be expressed as: (3.12) where s(t) is slowly time varying compared to the carrier . For, where B is the bandwidth of s(t), we can write: (3.13) In the last equation 3.13, the so-called wave vector was introduced, and its magnitude is the wavenumber. One can also write, where is the wavelength. Make sure that k also points in the direction of propagation, e.g., in the x-y plane we can get: (3.14) where is the direction of propagation, defined counter clockwise relative the x axis. It should be noted that 3.12 implicitly assumed far-field conditions, since an isotropic, which refers to uniform propagation/transmission in all directions, point source gives rise to a spherical traveling wave whose amplitude is inversely proportional to the distance to the source. All points lying on the surface of a sphere of radius R will then share a common phase and are referred to as a wave front. This indicates that the distance between the emitters and the receiving antenna array determines whether the spherical degree of the wave should be taken into account. The reader is referred to e.g., [10, 24] for treatments of near field reception. Far field receiving conditions imply that the radius of propagation is so large that a flat plane of constant phase can be considered, thus resulting in a plane wave as indicated in Eq. 8. Though not necessary, the latter will be our assumed working mode l for convenience of exposition. Note that a linear medium implies the validity of the superposition principle, and thus allows for more than one traveling wave. Equation 8 carries both spatial and temporal information and represents an adequate model for distinguishing signals with distinct spatial-temporal parameters. These may come in various forms, such as DOA, in general azimuth and elevation, signal polarization, transmitted waveforms, temporal frequency etc. Each emitter is generally associated with a set of such characteristics. The interest in unfolding the signal parameters forms the essence of sensor array signal processing as presented herein, and continues to be an important and active topic of research. 3.4 Smart antenna Smart antennas are devices which adapt their radiation pattern to achieve improved performance either range or capacity or some combination of these [1]. The rapid growth in demand for mobile communications services has encouraged research into the design of wireless systems to improve spectrum efficiency, and increase link quality [7]. Using existing methods more effective, the smart antenna technology has the potential to significantly increase the wireless. With intelligent control of signal transmission and reception, capacity and coverage of the mobile wireless network, communications applications can be significantly improved [2]. In the communication system, the ability to distinguish different users is essential. The smart antenna can be used to add increased spatial diversity, which is referred to as Space Division Multiple Access (SDMA). Conventionally, employment of the most common multiple access scheme is a frequency division multiple access (FDMA), Time Division Multiple Access (TDMA), and Code Division Multiple Access (CDMA). These independent users of the program, frequency, time and code domain were given three different levels of diversity. Potential benefits of the smart antenna show in many ways, such as anti-multipath fading, reducing the delay extended to support smart antenna holding high data rate, interference suppression, reducing the distance effect, reducing the outage probability, to improve the BER (Bit Error Rate)performance, increasing system capacity, to improve spectral efficiency, supporting flexible and efficient handoff to expand cell coverage, flexible management of the district, to extend the battery life of mobile station, as well as lower maintenance and operating costs. 3.4.1 Types of Smart Antennas The environment and the systems requirements decide the type of Smart Antennas. There are two main types of Smart Antennas. They are as follows: Phased Array Antenna In this type of smart antenna, there will be a number of fixed beams between which the beam will be turned on or steered to the target signal. This can be done, only in the first stage of adjustment to help. In other words, as wanted by the moving target, the beam will be the Steering [2]. Adaptive Array Antenna Integrated with adaptive digital signal processing technology, the smart antenna uses digital signal processing algorithm to measure the signal strength of the beam, so that the antenna can dynamically change the beam which transmit power concentrated, as figure 3.2 shows. The application of spatial processing can enhance the signal capacity, so that multiple users share a channel. Adaptive antenna array is a closed-loop feedback control system consisting of an antenna array and real-time adaptive signal receiver processor, which uses the feedback control method for automatic alignment of the antenna array pattern. It formed nulling interference signal offset in the direction of the interference, and can strengthen a useful signal, so as to achieve the purpose of anti-jamming [3]. Figure 2 click for text version Figure 3.2 3.4.2 Advantages and disadvantages of smart antenna Advantages First of all, a high level of efficiency and power are provided by the smart antenna for the target signal. Smart antennas generate narrow pencil beams, when a big number of antenna elements are used in a high frequency condition. Thus, in the direction of the target signal, the efficiency is significantly high. With the help of adaptive array antennas, the same amount times the power gain will be produce, on condition that a fixed number of antenna elements are used. Another improvement is in the amount of interference which is suppressed. Phased array antennas suppress the interference with the narrow beam and adaptive array antennas suppress by adjusting the beam pattern [2]. Disadvantages The main disadvantage is the cost. Actually, the cost of such devices will be more than before, not only in the electronics section, but in the energy. That is to say the device is too expensive, and will also decrease the life of other devices. The receiver chains which are used must be decreased in order to reduce the cost. Also, because of the use of the RF electronics and A/D converter for each antenna, the costs are increasing. Moreover, the size of the antenna is another problem. Large base stations are needed to make this method to be efficient and it will increase the size, apart from this multiple external antennas needed on each terminal. Then, when the diversity is concerned, disadvantages are occurred. When mitigation is needed, diversity becomes a serious problem. The terminals and base stations must equip with multiple antennas. 3.5 White noise White noise is a random signal with a flat power spectral density []. In another word, the signal contains the equal power within a particular bandwidth at the centre frequency. White noise draws its name from white light where the power spectral density of the light is distributed in the visible band. In this way, the eyes three colour receptors are approximately equally stimulated []. In statistical case, a time series can be characterized as having weak white noise on condition that {} is a sequence of serially uncorrelated random vibrations with zero mean and finite variance. Especially, strong white noise has the quality to be independent and identically distributed, which means no autocorrelation. In particular, the series is called the Gaussian white noise [1], if is normally distributed and it has zero mean and standard deviation. Actually, an infinite bandwidth white noise signal is just a theoretical construction which cannot be reached. In practice, the bandwidth of white noise is restricted by the transmission medium, the mechanism of noise generation, and finite observation capabilities. If a random signal is observed with a flat spectrum in a mediums widest possible bandwidth, we will refer it as white noise. 3.5.1 Mathematical definition White random vector A random vector W is a white random vector only if its mean vector and autocorrelation matrix are corresponding to the follows: mu_w = mathbb{E}{ mathbf{w} } = 0 (3.15) R_{ww} = mathbb{E}{ mathbf{w} mathbf{w}^T} = sigma^2 mathbf{I} . (3.16) That is to say, it is a zero mean random vector, and its autocorrelation matrix is a multiple of the identity matrix. When the autocorrelation matrix is a multiple of the identity, we can regard it as spherical correlation. White random process A time continuous random process where is a white noise signal only if its mean function and autocorrelation function satisfy the following equation: mu_w(t) = mathbb{E}{ w(t)} = 0 (3.17) R_{ww}(t_1, t_2) = mathbb{E}{ w(t_1) w(t_2)} = (N_{0}/2)delta(t_1 t_2). (3.18) That is to say, it is zero mean for all time and has infinite power at zero time shift since its autocorrelation function is the Dirac delta function. The above autocorrelation function implies the following power spectral density. Since the Fourier transform of the delta function is equal to 1, we can imply: S_{ww}(omega) = N_{0}/2 ,! (3.19) Since this power spectral density is the same at all frequencies, we define it white as an analogy to the frequency spectrum of white light. A generalization to random elements on infinite dimensional spaces, e.g. random fields, is the white noise measure. 3.5.2 Statistical properties The white noise is uncorrelated in time and does not restrict the values a signal can take. Any distribution of values about the white noise is possible. Even a so-called binary signal that can only take the values of 1 or -1 will be white on condition that the sequence is statistically uncorrelated. Any noise with a continuous distribution, like a normal distribution, can be white noise certainly. It is often incorrectly assumed that Gaussian noise is necessarily white noise, yet neither property implies the other. Gaussianity refers to the probability distribution with respect to the value, in this context the probability of the signal reaching amplitude, while the term white refers to the way the signal power is distributed over time or among frequencies. Spectrogram of pink noise (left) and white noise (right), showed with linear frequency axis (vertical). We can therefore find Gaussian white noise, but also Poisson, Cauchy, etc. white noises. Thus, the two words Gaussian and white are often both specified in mathematical models of systems. Gaussian white noise is a good approximation of many real-world situations and generates mathematically tractable models. These models are used so frequently that the term additive white Gaussian noise has a standard abbreviation: AWGN. White noise is the generalized mean-square derivative of the Wiener process or Brownian motion. 3.6 Normal Distribution In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution that has a bell-shaped probability density function, known as the Gaussian function or informally as the bell curve[1]. f(x;mu,sigma^2) = frac{1}{sigmasqrt{2pi}} e^{ -frac{1}{2}left(frac{x-mu}{sigma}right)^2 } The parameter ÃŽÂ ¼ is the mean or expectation (location of the peak) and à Ã†â€™Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °2 is the variance. à Ã†â€™ is known as the standard deviation. The distribution with ÃŽÂ ¼ = 0 and à Ã†â€™Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °2 = 1 is called the standard normal distribution or the unit normal distribution. A normal distribution is often used as a first approximation to describe real-valued random variables that cluster around a single mean value. http://upload.wikimedia.org/wikipedia/commons/thumb/8/8c/Standard_deviation_diagram.svg/325px-Standard_deviation_diagram.svg.png The normal distribution is considered the most prominent probability distribution in statistics. There are several reasons for this:[1] First, the normal distribution arises from the central limit theorem, which states that under mild conditions, the mean of a large number of random variables drawn from the same distribution is distributed approximately normally, irrespective of the form of the original distribution. This gives it exceptionally wide application in, for example, sampling. Secondly, the normal distribution is very tractable analytically, that is, a large number of results involving this distribution can be derived in explicit form. For these reasons, the normal distribution is commonly encountered in practice, and is used throughout statistics, natural sciences, and social sciences [2] as a simple model for complex phenomena. For example, the observational error in an experiment is usually assumed to follow a normal distribution, and the propagation of uncertainty is computed using this assumption. Note that a normally distributed variable has a symmetric distribution about its mean. Quantities that grow exponentially, such as prices, incomes or populations, are often skewed to the right, and hence may be better described by other distributions, such as the log-normal distribution or Pareto distribution. In addition, the probability of seeing a normally distributed value that is far (i.e. more than a few standard deviations) from the mean drops off extremely rapidly. As a result, statistical inference using a normal distribution is not robust to the presence of outliers (data that are unexpectedly far from the mean, due to exceptional circumstances, observational error, etc.). When outliers are expected, data may be better described using a heavy-tailed distribution such as the Students t-distribution. 3.6.1 Mathematical Definition The simplest case of a normal distribution is known as the standard normal distribution, described by the probability density function phi(x) = frac{1}{sqrt{2pi}}, e^{- frac{scriptscriptstyle 1}{scriptscriptstyle 2} x^2}. The factor scriptstyle 1/sqrt{2pi} in this expression ensures that the total area under the curve à Ã¢â‚¬ ¢(x) is equal to one[proof], and 12 in the exponent makes the width of the curve (measured as half the distance between the inflection points) also equal to one. It is traditional in statistics to denote this function with the Greek letter à Ã¢â‚¬ ¢ (phi), whereas density functions for all other distributions are usually denoted with letters f or p.[5] The alternative glyph à Ã¢â‚¬   is also used quite often, however within this article à Ã¢â‚¬   is reserved to denote characteristic functions. Every normal distribution is the result of exponentiating a quadratic function (just as an exponential distribution results from exponentiating a linear function): f(x) = e^{a x^2 + b x + c}. , This yields the classic bell curve shape, provided that a 0 everywhere. One can adjust a to control the width of the bell, then adjust b to move the central peak of the bell along the x-axis, and finally one must choose c such that scriptstyleint_{-infty}^infty f(x),dx = 1 (which is only possible when a Rather than using a, b, and c, it is far more common to describe a normal distribution by its mean ÃŽÂ ¼ = à ¢Ã‹â€ Ã¢â‚¬â„¢Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °b2a and variance à Ã†â€™2 = à ¢Ã‹â€ Ã¢â‚¬â„¢Ãƒ ¢Ã¢â€š ¬Ã¢â‚¬ °12a. Changing to these new parameters allows one to rewrite the probability density function in a convenient standard form, f(x) = frac{1}{sqrt{2pisigma^2}}, e^{frac{-(x-mu)^2}{2sigma^2}} = frac{1}{sigma}, phi!left(frac{x-mu}{sigma}right). For a standard normal distribution, ÃŽÂ ¼ = 0 and à Ã†â€™2 = 1. The last part of the equation above shows that any other normal distribution can be regarded as a version of the standard normal distribution that has been stretched horizontally by a factor à Ã†â€™ and then translated rightward by a distance ÃŽÂ ¼. Thus, ÃŽÂ ¼ specifies the position of the bell curves central peak, and à Ã†â€™ specifies the width of the bell curve. The parameter ÃŽÂ ¼ is at the same time the mean, the median and the mode of the normal distribution. The parameter à Ã†â€™2 is called the variance; as for any random variable, it describes how concentrated the distribution is around its mean. The square root of à Ã†â€™2 is called the standard deviation and is the width of the density function. The normal distribution is usually denoted by N(ÃŽÂ ¼,à ¢Ã¢â€š ¬Ã¢â‚¬ °Ãƒ Ã†â€™2).[6] Thus when a random variable X is distributed normally with mean ÃŽÂ ¼ and variance à Ã†â€™2, we write X sim mathcal{N}(mu,,sigma^2). , 3.6.2 Alternative formulations Some authors advocate using the precision instead of the variance. The precision is normally defined as the reciprocal of the variance (à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢2), although it is occasionally defined as the reciprocal of the standard deviation (à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢1).[7] This parameterization has an advantage in numerical applications where à Ã†â€™2 is very close to zero and is more convenient to work with in analysis as à Ã¢â‚¬Å¾ is a natural parameter of the normal distribution. This parameterization is common in Bayesian statistics, as it simplifies the Bayesian analysis of the normal distribution. Another advantage of using this parameterization is in the study of conditional distributions in the multivariate normal case. The form of the normal distribution with the more common definition à Ã¢â‚¬Å¾ = à Ã†â€™Ãƒ ¢Ã‹â€ Ã¢â‚¬â„¢2 is as follows: f(x;,mu,tau) = sqrt{frac{tau}{2pi}}, e^{frac{-tau(x-mu)^2}{2}}. The question of which normal distribution should be called the standard one is also answered differently by various authors. Starting from the works of Gauss the standard normal was considered to be the one with variance à Ã†â€™2 = 12 : f(x) = frac{1}{sqrtpi},e^{-x^2} Stigler (1982) goes even further and insists the standard normal to be with the variance à Ã†â€™2 = 12à Ã¢â€š ¬ : f(x) = e^{-pi x^2} According to the author, this formulation is advantageous because of a much simpler and easier-to-remember formula, the fact that the pdf has unit height at zero, and simple approximate formulas for the quintiles of the distribution. 3.7 Cramer-Rao Bound In estimation theory and statistics, the Cramà ©r-Rao bound (CRB) or Cramà ©r-Rao lower bound (CRLB), named in honor of Harald Cramer and Calyampudi Radhakrishna Rao who were among the first to derive it,[1][2][3] expresses a lower bound on the variance of estimators of a deterministic parameter. The bound is also known as the Cramà ©r-Rao inequality or the information inequality. In its simplest form, the bound states that the variance of any unbiased estimator is at least as high as the inverse of the Fisher information. An unbiased estimator which achieves this lower bound is said to be (fully) efficient. Such a solution achieves the lowest possible mean squared error among all unbiased methods, and is therefore the minimum variance unbiased (MVU) estimator. However, in some cases, no unbiased technique exists which achieves the bound. This may occur even when an MVU estimator exists. The Cramà ©r-Rao bound can also be used to bound the variance of biased estimators of given bias. In some cases, a biased approach can result in both a variance and a mean squared error that are below the unbiased Cramà ©r-Rao lower bound; see estimator bias. statement The Cramà ©r-Rao bound is stated in this section for several increasingly general cases, beginning with the case in which the parameter is a scalar and its estimator is unbiased. All versions of the bound require certain regularity conditions, which hold for most well-behaved distributions. These conditions are listed later in this section. Scalar unbiased case Suppose theta is an unknown deterministic parameter which is to be estimated from measurements x, distributed according to some probability density function f(x;theta). The variance of any unbiased estimator hat{theta} of theta is then bounded by the reciprocal of the Fisher information I(theta): mathrm{var}(hat{theta}) geq frac{1}{I(theta)} where the Fisher information I(theta) is defined by I(theta) = mathrm{E} left[ left( frac{partial ell(x;theta)}{partialtheta} right)^2 right] = -mathrm{E}left[ frac{partial^2 ell(x;theta)}{partialtheta^2} right] and ell(x;theta)=log f(x;theta) is the natural logarithm of the likelihood function and mathrm{E} denotes the expected value. The efficiency of an unbiased estimator hat{theta} measures how close this estimators variance comes to this lower bound; estimator efficiency is defined as e(hat{theta}) = frac{I(theta)^{-1}}{{rm var}(hat{theta})} or the minimum possible variance for an unbiased estimator divided by its actual variance. The Cramà ©r-Rao lower bound thus gives e(hat{theta}) le 1. General scalar case A more general form of the bound can be obtained by considering an unbiased estimator T(X) of a function psi(theta) of the parameter theta. Here, unbiasedness is understood as stating that E{T(X)} = psi(theta). In this case, the bound is given by mathrm{var}(T) geq frac{[psi'(theta)]^2}{I(theta)} where psi'(theta) is the derivative of psi(theta) (by theta), and I(theta) is the Fisher information defined above. Bound on the variance of biased estimators Apart from being a bound on estimators of functions of the parameter, this approach can be used to derive a bound on the variance of biased estimators with a given bias, as follows. Consider an estimator hat{theta} with biasb(theta) = E{hat{theta}} theta, and let psi(theta) = b(theta) + theta. By the result above, any unbiased estimator whose expectation is psi(theta) has variance greater than or equal to (psi'(theta))^2/I(theta). Thus, any estimator hat{theta} whose bias is given by a function b(theta) satisfies mathrm{var} left(hat{theta}right) geq frac{[1+b'(theta)]^2}{I(theta)}. The unbiased version of the bound is a special case of this result, with b(theta)=0. Its trivial to have a small variance à ¢Ã‹â€ Ã¢â‚¬â„¢ an estimator that is constant has a variance of zero. But from the above equation we find that the mean squared errorof a biased estimator is bounded by mathrm{E}left((hat{theta}-theta)^2right)geqfrac{[1+b'(theta)]^2}{I(theta)}+b(theta)^2, using the standard decomposition of the MSE. Note, however, that this bound can be less than the unbiased Cramà ©r-Rao bound 1/I(ÃŽÂ ¸). See the example of estimating variance below. Multivariate case Extending the Cramà ©r-Rao bound to multiple parameters, define a parameter column vector boldsymbol{theta} = left[ theta_1, theta_2, dots, theta_d right]^T in mathbb{R}^d with probability density function f(x; boldsymbol{theta}) which satisfies the two regularity conditions below. The Fisher information matrix is a d times d matrix with element I_{m, k} defined as I_{m, k} = mathrm{E} left[ frac{d}{dtheta_m} log fleft(x; boldsymbol{theta}right) frac{d}{dtheta_k} log fleft(x; boldsymbol{theta}right) right]. Let boldsymbol{T}(X) be an estimator of any vector function of parameters, boldsymbol{T}(X) = (T_1(X), ldots, T_n(X))^T, and denote its expectation vector mathrm{E}[boldsymbol{T}(X)] by boldsymbol{psi}(boldsymbol{theta}). The Cramà ©r-Rao bound then states that the covariance matrix of boldsymbol{T}(X) satisfies mathrm{cov}_{boldsymbol{theta}}left(boldsymbol{T}(X)right) geq frac {partial boldsymbol{psi} left(boldsymbol{theta}right)} {partial boldsymbol{theta}} [Ileft(boldsymbol{theta}right)]^{-1} left( frac {partial boldsymbol{psi}left(boldsymbol{theta}right)} {partial boldsymbol{theta}} right)^T where The matrix inequality A ge B is understood to mean that the matrix A-B is positive semi definite, and partial boldsymbol{psi}(boldsymbol{theta})/partial boldsymbol{theta} is the Jacobian matrix whose ijth element is given by partial psi_i(boldsymbol{theta})/partial theta_j. If boldsymbol{T}(X) is an unbiased estimator of boldsymbol{theta} (i.e., boldsymbol{psi}left(boldsymbol{theta}rig

The Apa Ethical Principles for Psychologists and Code of Conduct

The APA Ethical Principles for Psychologists and Code of Conduct: Cultural Sensitivity and Diversity – is the code culturally encapsulated and biased? Emmanuel Mueke Author Note Emmanuel Mueke. Independent Researcher. Correspondence regarding this article should be addressed to Emmanuel Mueke, P. O. Box 44935 – 00100. Nairobi, Kenya. Contact: [email  protected] com Abstract This paper explores the American Psychological Association (APA) Ethical Principles for Psychologists and Code of Conduct as regards the issue of multicultural and diverse professional practise.Its aim is to establish whether diversity and cultural variety and differences are adequately provided for in the body of the document. Psychologists are mandated to provide services to a multitude of culturally diverse and varied clients in a manner that is both professional and ethical. In such situations cultural sensitivity is fundamental and has been elevated to best practice. The code has been question ed as to the efficacy of its cultural sensitivity; firstly in terms of whether the code itself is culturally encapsulated and secondly whether there exists an explicit or implicit cultural bias.To address this issue we shall undertake a look at the code; its inherent limitations and shortcomings. Secondly the issue of the importance of cultural sensitivity and its translated application in matters of ethical service delivery shall be addressed. Keywords: APA Ethical Principles for Psychologists and Code of Conduct, ethics, multicultural, diversity, bias. The APA Ethical Principles for Psychologists and Code of Conduct: Cultural Sensitivity and Diversity – is the code culturally encapsulated and biased?Cultural sensitivity and professional ethics are central to the provision of psychologists’ services; this has led to the APA issuing guidelines in an effort to ensure that best practice is not only aspired to but more importantly achieved. This paper examines the Code of Conduct and the pursuant Guidelines on Multicultural Education, Training, Research, Practice, and Organizational Change for Psychologists (APA, 2002). Analysis of these documents will establish the existence of mechanisms to ensure protection against cultural bias and effective promotion of cultural sensitivity.Literature Review In the 2002 APA Ethical Principles for Psychologists and Code of Conduct several principles were outlined to ensure that cultural sensitivity was adopted as the guiding policy for practicing psychologists. The first mention of the issue of diversity and its effect on professional practice is in Principle E, which engenders awareness of and respect for cultural differences and admonishes the practitioners to try and eliminate the effect of biases upon their work and not to condone any activities of others based on prejudice. Further under Section 3. 1, unfair discrimination on any basis including culture is prohibited, combined with Section 3. 03 which admon ishes the practitioners from engaging in any behaviour that would be demeaning to a person of different culture. The issue of ethical provision of services is not just about preventing discrimination or harassment to persons of different cultures but it is also about ensuring that they are provided with adequate and competent services as they well deserve; to this effect Section 2. 01 provides what has been termed a boundary of competence.The boundary is intended to ensure that the services provided are effective in the specific circumstances faced; to this effect first it limits a psychologist to only undertake to provide services within the boundary of his expertise, education and experience and secondly it mandates that a psychologist must undertake the training or education necessary to provide the requisite services to the target populace, this training or education taking into account all factors that have a bearing on effective service delivery such as age, gender, ethnicity et cetera.Lastly under Section 9. 06 (APA, 2002) when interpreting assessment results a psychologist is mandated to take into account all the factors relevant, including the cultural differences of the assessment subject, that might nuance the results in any way. To translate these into effective practice the APA published the Guidelines on Multicultural Education, Training, Research, Practice, and Organizational Change for Psychologists (APA, 2002); which was meant to embody diversity aspirations for professionals.This document built on the precedent established by the Guidelines for providers of psychological services to ethnic, linguistic, and culturally diverse populations (APA, 1990). It translated the Principles previously outlined into six different guideline rules with the appropriate commentary on the way to best achieve such targets. The guidelines are; 1. Psychologists are encouraged to recognize that, as cultural beings, they may hold attitudes and beliefs that can detri mentally influence their perceptions of and interactions with individuals who are ethnically and racially different from themselves 2.Psychologists are encouraged to recognize the importance of multicultural sensitivity/responsiveness, knowledge, and understanding about ethnically and racially different individuals 3. As educators, psychologists are encouraged to employ the constructs of multiculturalism and diversity in psychological education 4. Culturally sensitive psychological researchers are encouraged to recognize the importance of conducting culture-centred and ethical psychological research among persons from ethnic, linguistic, and racial minority backgrounds 5.Psychologists strive to apply culturally-appropriate skills in clinical and other applied psychological practices 6. Psychologists are encouraged to use organizational change processes to support culturally informed organizational (policy) development and practices Discussion The Guidelines admit the existence of a Eurocentric bias in the psychological profession and posit themselves as an ever-evolving solution; changing as further empirical research on the issue is undertaken.Moreover the document places a time limit on its validity in order to spur further research on the issue of multicultural practice. In order to ensure its efficacy the APA set up a task force whose sole purpose was to look into the implementation of the guidelines with a view to providing proper feedback by identifying pertinent implementation and infusion recommendations. The task force produced a report on the infusion of the paradigm shift in service delivery outlining how this should be undertaken; Report of the APA Task Force on the Implementation of the Multicultural Guidelines (APA, 2008).The report split the guidelines into two categories the first being those whose implementation fell unto the practitioners and into this category they placed the first and second guidelines. The rest were in the category of thos e whose implementation required facilitation by the APA both in terms of administrative structures and funding; for example the APA was tasked with establishing an Office of Diversity Enhancement and hiring a Chief Diversity Officer to run it. The Office’s purpose is ensuring that there is diversity across the organization which helps with the ethical provision of services across multicultural diversity.Conclusion Having gone through the Code of Conduct, the pursuant Guidelines and the Implementation Report there is no evidence of cultural bias and encapsulation; rather there is incontrovertible evidence of contrived and concerted efforts to address the bias existent in the profession and its philosophy. References American Psychological Association. (1990). Guidelines for providers of psychological services to ethnic, linguistic, and culturally diverse populations. Washington, DC: Author. Retrieved from www. apa. org/pi/oema/guide. html American Psychological Association. (2 002).Ethical principles of psychologists and code of conduct. American Psychologist, 57, 1060-1073. Retrieved from www. apa. org/ethics. code. html American Psychological Association. (2003). Guidelines on multicultural education, training, research, practice, and organizational change for psychologists. American Psychologist, 58, 377-402. (See www. apa. org/pi/multiculturalguidelines/homepage. html) American Psychological Association. (2008). Report of the Task Force on the Implementation of the Multicultural Guidelines. Washington, DC: Author. Retrieved from http://www. apa. org/pi/

Student Engagement At Felician University - 1468 Words

As I finish up my practicum hours this week, I am amazed at how everything has come full circle. As someone who came into college with no clue of what I wanted to do after graduation, I found that the Office of Student Engagement allowed me to develop as a person and figure out the things that are most important to me. As I started to get more involved on-campus and take on more leadership roles, I started to gain an interest in working in the field of higher education in hopes to provide this same experience to other college students. By having the opportunity to work with Patrick Dezort, the Director of Student Development and Engagement at Felician University, as part of my Field Practicum in Psychology course, I was hoping to get†¦show more content†¦Although I was hesitant at first to perform certain tasks without getting permission to do so, I found that as I got more comfortable in the office, I was more willing to take on more tasks. Ultimately, I believe that t aking initiative in this setting is an important concept to grasp, since the field of student affairs involves a lot of problem solving and critical thinking skills. In my experience working with college students, I have found that this population has a lot of complex needs. Although the Office of Student Engagement tries to do its best to provide activities that appeal to most students’ interests, it is difficult to plan events that everyone likes within everyone’s schedules. I have also learned that because this age group is undergoing a lot of growth and development, the maturity levels among students vary, which means that these students will approach the same type of situation in a variety of different ways. Furthermore, I found that while there are many programs and policies in place to help college students excel, college administrators (both within and outside of Felician University) disagree on the most effective ways to advocate for and serve college stude nts. As someone who hopes to continue into thisShow MoreRelatedA Student s First Year Of College Essay2204 Words   |  9 Pagesdecades. With many professions now requiring a college education, more students are enrolled in college today than ever before. However, just because more students are enrolled in college does not mean that more people are on the path to success. Attending college can present a plethora of obstacles for students due to a variety of different reasons. As leaders in higher education work to develop strategies to support students towards their college degree, it is imperative that we understand andRead MoreA Tale Of Two Campuses : Student Achievement At Colleges With Multiple Campuses Essay2128 Words   |  9 PagesA Tale of Two Campuses: Student Achievement at Colleges with Multiple Campuses Literature Review A student’s first-year of college is arguably one of the most critical years in a student’s collegiate career. When students perform well in their first-year of college, they are significantly more likely to continue towards and earn their degree. Therefore, it is especially important that higher education officials analyze ways to improve upon a student’s initial college experience. Kuh, Cruce

Analysis Of Poetry By Sylvia Plath - 1374 Words

Poetry to some is the frustration of a riddle that cannot be solved. To others, it is the joy one feels while solving the same riddle. A writer has the power to convey certain themes and ideas within a poem in a span of one line or a hundred lines. They can create the tone for the poem with the help of a single word, or a comma placed in the correct spot. With the use of figures of speech such as metaphor, a writer can give the reader images and compare different ideas that have similar qualities that help the reader deduce what the poem is about. Poetry has many ways in which a writer can make a series of words and lines form together to create a story with a meaning that the reader has to dig deep into the folds of the poem to find. Sylvia Plath creates a riddle to be solved by the reader in her poem â€Å"Metaphors† with the use of elaborate metaphors, select word choice, and strict poem structure to convey what the poem is about and what the speaker of the poem is feeling. 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We use it to convey love, to mourn a loss, tell a story, or to say the things we are afraid to tell an actual person. Emily Dickinson and Sylvia Plath dont write sonnets. These two poets clearly used poetry as a cathartic release for the troubles of their lives. Their struggles with even the rudimentary, plagued them throughout their short lifetime. Life and death being in constantRead MoreMutilating Self Into Spirit: Sylvia Plaths Poems.4131 Words   |  17 PagesSylvia Plath’s poems: Translation of the self into spirit, after an ordeal of mutilation. Introduction of the poems and the essay: * â€Å"Daddy† Sylvia Plath uses her poem, â€Å"Daddy†, to express intense emotions towards her father’s life and death and her disastrous relationship with her husband. The speaker in this poem is Sylvia Plath who has lost her father at age ten, at a time when she still adored him unconditionally. 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Analysis Of The Kite Runner And A Thousand Splendid Suns

Note from the Author: This story is based on the novels, The Kite Runner and A Thousand Splendid Suns, by Khaled Hosseini. Set in Kabul, Afghanistan, both stories revolve around the country’s political struggles. In The Kite Runner, Sohrab, the child of Hassan and Farzana, is placed in an orphanage, run by Zaman, after his parents are killed. In the same orphanage that Aziza, daughter of Laila, one of the protagonists of A Thousand Splendid Suns, is left at. Aziza was placed in the orphanage because her family could only support one child and her â€Å"father† favored her brother because he was a boy. Laila lives with Mariam, the first wife of her husband Rasheed, and a second mother to her children. Sitting outside the worn†¦show more content†¦Weeks ago, Aziza had let go of her intrepid exuberance, in exchange for the passivity of someone whose life depended on the very people who wanted her dead. Cautiously, Mariam approached Laila, whose lowered eyes and furrowed brows had given away her disconsolate state. â€Å"She will be okay. She is a strong girl, nearly as strong as you.† Mariam assured her. Smiling halfheartedly, Laila nodded. Because her emotions overwhelmed her and because she did not feel she could say the right thing, she did not want to speak. Having gone through more than one could imagine, Laila knew that no child should need to be as strong as she was. After she stood up, Laila saw Zaman’s slumped figure. Needing to speak to him, she shouted, â€Å"Zaman.† One of their last remaining windows had been shattered and the acrid smell of mold had filled the building. Consequently, Laila felt that she should confront Zaman. â€Å"How can you run such a place?† She demanded, even though she knew exactly how. Intimidated, Zaman shrunk under Laila’s invidious gaze. Laila knew what kind of funding Zaman had and it was a pittance in comparison to what Zaman deserved, but if she could not provide for her daughter, she could at least make sure someone else did. â€Å"Look at my daughter. You can see her ribs through that cloth that you have given her instead of clothing.† Pleading, Zaman whimpered, â€Å" We have nothing more to give.† Laila shook her head, not at Zaman but at her society itself. In Kabul, these brokenShow MoreRelatedReview Of A Thousand Splendid Suns Essay3732 Words   |  15 PagesAFTER EFFECTS OF WAR IN KABUL AS BASED ON A THOUSAND SPLENDID SUNS In partial fulfillment of the requirements for Award of Degree of Bachelors of Arts (Hons.) in English Submitted By: Supervised By: Sahib Alam Shaily Dabra Ma’am SYNOPSIS The title of the current research is ‘the after-effects of war in Kabul’ based on the novel-A THOUSAND SPLENDID SUNS. The novel portrays the theme of war and it’sRead MorePersonal Project4460 Words   |  18 PagesSources Being Used †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. Page 6. Mind Map †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. Page 6. Justification of Techniques †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. Page 7. Description of Process (Analysis)†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.. Page 8. Analysis of Research †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. Page 9. Description of my Inspiration †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦. Page 9. Evaluation of Product (Reflection)†¦...†¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦.†¦. Page 10. Conclusion †¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦Ã¢â‚¬ ¦

Bullying Essay/Story Free Essays

Note: This is an essay based on a true story. This was used in a olo provinical exam in the past to help prepare for my English 10 provincials this past summer. Names were changed for identification reasons. We will write a custom essay sample on Bullying Essay/Story or any similar topic only for you Order Now Bullying is one of the most common issues in today’s society. Bullying occurs in school, work, or our own neighbourhood. Bullying also occurs online. Bullying is one of the hardest things to get over, and this is my story. ~Intro ~ During the years I was bullied in school, I had a time sticking up for myself and did not feel completely about myself. I have been called names, been target for pranks, and I know was physically and sexually harassed by my peers and classmate. The only two people I can trust is my best friend since 8th grade Rue and my grandmother (who is my guardian). When something happen to me during the school day, I try to be silent about it and try not to let it bother me. My way of getting away from bullying is listening to music on my iPod. ~Chapter 1~ Grade 8 was the most emotional, stressful year I had in all of my school years. This is because this one girl named Clove, was ruining my life. She would tell anyone I was trying to get to know that I had some sort of diseases and advice them to stay away from me and forces them to be friends with her. She took one of my good friend from dance in the past. She made everyone of my friends to go against me, except for Rue. She stayed with me until the end. Her and I are still great friends today. We may not go to the same high school now, but we try to see each other whenever we can. ~Chapter 2~ Clove, however, got meaner everyday. One time, her and her friend Glimmer wanted to talk to me after our class made cookies in Home Ec near he end of the day. We were allow to take the cookies home to our family and put them in a bag. Both Glimmer and Clove had one paper bag. I said yes then they took me outside under the huge Oak trees behind the school. Clove asked Glimmer to leave her and I some privacy. Glimmer left us after that. Clove asked me if I saw Clove’s boyfriend-at-the-time Peeta a couple days ago and kissed him. I already knew that answer to her question because I was doing my homework on that night. So I said no. Clove was assuming that I was lying to her and stared each other for a few minutes. What I didn’t know is that Glimmer was sneaky creeping up to me and dumped an entire flag on me. Clove and Glimmer ran away laughing, leaving me shocked and completely covered in flour. I ran to the classroom (and it was also raining that day too) angry and furious. When I walk into my homeroom classroom, everyone was shocked and asked what happen. I told my teacher Ms. Kennedy what happened to me and made Clove and Glimmer stay in the office and sent me to the principal office. ~Chapter 3~ Our principal Mr. Burwell, couldn’t believe what he has seen when I walk into his office that day. When I sat on the extra chairs in his office, little traces of flour from the top of my head fell down to the chair as if the flour was snowflakes falling down from the sky. He asked me what happen to me and I told him the story. He was completely stunned. Ms Kennedy walked into Mr. Burwell a few seconds later after I told him what happen. Ms. Kennedy asked me who was picking me up and I sad my grandpa is picking me up. My grandpa’s 2009 Hyundai navy Elentra was sitting in drop off zone, waiting for me to come out. Ms. Kennedy then ran outside and the pouring rain and told my grandpa to come inside to the office. He was confused and wondered why he needs to come inside, that’s when he saw me in the principal office, covered in flour. My grandpa was shocked and ask what happen. My head started to irritate me and scratched my head as hard as I can until my scalp started to sting and bleed from the flour. The white small snowflakes from head continue to fall into the chair. Mr. Burwell asked Ms. Kennedy to take me to the handicap washroom (which was located by nearby his office) to try to take some flour off of my face while he explains my grandpa why I was covered in flour. When Ms. Kennedy was trying help me to take some of the flour off my face, it irritated my skin. I just want to rip off my skin and let myself bleed to death. ~Chapter 4:~ When my grandpa and I came home from the principal’s office, I ran upstairs to my living room and burst into tears. I didn’t understand why Clove was doing this to me. I mean, why me? When the last month of school came along, it was the worst month of my life. This is all started when Clove decided to throw a birthday party for Rue. One of the food that was â€Å"suppose† to be provided at the party was a look-a-like oatmeal cookies, but they didn’t taste like oatmeal at all. Clove, Glimmer, Cato, and Marvel (or I call Clove’s friends the â€Å"Career Tributes†) forced me eat this cookie that was filled with dead insect, dust, dirt, broken eggs shells, you name it. I felt sick for 2 weeks after that incident. ~Chapter 5~ I went to Rue’s part a couple days after the cookies incident. I bought a nice, brand new outfit for Rue’s party. A blue tank top with lace on the top and on the bottom on the shirt, a dark grey skirt from American Eagle, a light black jacket to go on top of my shirt, and my black Franco Santo wedges I got from my grandma for Easter when she went to Seattle a week before Easter. The party began at an Italian restaurant. I had a terrible time at the restaurant because Cato â€Å"accidentally† split pasta sauce from the meal he ordered onto my brand new top. I knew pasta sauce was hard to remove on clothes. We later went to Clove’s house, which it is not far from the restaurant. Clove told everyone that will be a water fight in an open field across the street from Clove’s house and told everyone to get change into their swimsuit. I, however, did not know there was going to be a water fight, but I remember Rue told me that the party is going to do something with water. During the water balloon fight, I had a hard time throwing the balloons at everyone because the size of the balloon was so big over my small child-like hands. The Career Tributes, including Clove, enjoyed this fight. Why? Because I was their. Of course, they had to use me as their target. After the fight, I was extremely cold and wet thought that I was going to get a nasty cold the next day. ~Chapter 6~ We then watch Drag Me To Hell, a horror movie. Rue hates horror movie. I gave Rue her birthday present while the movie was on. I got her a journal, and a book call Three Cups Of Tea. I also made her a homemade card, using my grandmother’s extra card stock and stamps she collected over the years. Rue loved her present and the card. Meanwhile, while everyone else was watching the movie. There was a killing scene on the TV. Everyone jump, including Cato, who split an entire glass of lemonade on my brand new skirt. I was so close of slapping him on the face. He spilt the lemonade juice on me on purpose. ~Chapter 7~ After I got home from the party was over, I went home with my Dad, and my sister Prim, angry and upset. I have to take action, but how? School was ending in 2 weeks, so what’s the point? Nearly two 2 weeks later, just a day before grade 8 grad, Clove and I got into a fight. She was violent and horrifying, like if she was going turn into a nasty beast. She punched me, scratched me and called me names. All I did was telling her what I thought about her. Telling her that she stole Peeta from me, I tried to fight back. , but I didn’t want to because I know that fighting is not a way to solve the problem. Her career tributes were right behind her to defend her. The rest of my classmates went along what Clove told them. Too scared to stick up for themselves. They were afraid of Clove and they did not want to go against her. They just watch me suffer. Rue was trying to break up the fight between me and Clove. The scratch on my left arm, nearby my elbow, turned into a scab. The scab drove me crazy and I couldn’t stop scratching it. I decided to turn the scab into a scar. To show people how violent Clove was really was. ~Chapter 8~ Finally, at last, Grad day came along. Unfortunately, though, I had to miss grad practice a couple hours before grad because of Clove. I had to sit in the office for the hour while the my grade 8 class was practicing for the ceremony. I hate the fact that I missed something that was once in a lifetime, but Mr. Burwell was only doing this to protect me. The rest of the day, everyone (except for the Career tributes and Clove) signed my year book. The torture was finally over. No more fear. No more hiding. No feeling like I want to take my own life and cutting myself. I can move from this nightmare and go to high school in peace. The best part of going to high school, is that I wouldn’t have to worry about Clove anymore. Epilogue~ In the end, the story is on my mind everyday lie it was grade 8 all over again. One of the thongs I learn is to speak to others. When I talk to someone about things, it makes me feel better. When I left middle school, Clove and her tributes mates left me a lot of damage, physically, mentally, and emotional. When I see my self in the mirror, I don’t feel beautiful and sees myself as an ugly person and wishes to have plastic surgery. When I do my hair and make-up, the comments of what Clove and her pack said to me would bother me. When I go clothes shopping, I would hate it because I’m not skinny and fit like Clove and Glimmer. to be honest, it’s hard to get over it. It will haunt me for years to come. ~Note~ If you notice on the names of the characters (expect for Mr. Burwell and Ms. Kennedy), you may recognize their names because they are from the Hunger Games by Suzanne Collions I look up to Katiness Everdeen (the main character in the novel) as a role model because she fought what she believed in and got through the worst through out the novel. I also got introduced the Hunger Games in grade 8 by my learning support teacher, by Mrs. Collions. How to cite Bullying Essay/Story, Essay examples

Accounting Information System Data Mining â€Myassignmenthelp.Com

Question: Discuss about the Accounting information Systemfor Data Mining. Answer: Role of Aata Analysis Tools and DataMining in Contemporary Organisations Raw data helps companies to process their day to day activities in a standard form. However, raw data is cannot be presented in result form. The structure of raw data is processed to derive results. There are numbers of transactions occurred in a business on daily basis and hence the data are stored in a standard form so that it can be processed and reports can be prepared for management reporting. All the transactions and information are stored in a database and used to prepare standard reports. Statistical analysis of data is a complex and multi-dimensional task. It requires a proper and deep knowledge to analyse and use the data to generate useful information for the business (Williams, 2011). It is important for businesses to always attentive towards the data and has command on the changing demand of the customers and the rapidly changing business environment. Businesses require reacting prompt and proactive in the current dynamic business environment so that it can sustain and s urvive in the market. Decisions are made on the basis of reports which are derived from the collected data. Managers are required first to have deep knowledge of data so that they can take appropriate and feasible dictions for the business. Data analysis tools help the businesses to store data in a standard from and maintain useful database for the businesses. It is not easy to memorise each and every transaction of business and therefore, the need for data analysis tool has increased (Wang, 2008). Both data analysis and mining are the most important part of business intelligence and are widely used for successfully running of businesses. Being an inseparable part of business a strong data warehousing strategies are required for the proper functioning of these verticals. Data analysis tools are more simple than data mining tools and require specialised staff to handle it. Whereas, data mining needs to be done by experts as the results are difficult to interpret and require other methods to verify the results (Wiley Ben, 2017). Data mining is a process of analysing and extracting from many perspectives and dimensions. Data mining is of two types first is descriptive mining which emphasises on existing data, second is predictive, which emphasises on data forecasting. It is a collection of the method which is used to develop interfaces from stored data (Maimon Rokach, 2010). The prime objective of data mining is concerned to gather the information and classify it into standard formats so that it can be used in future. The basis of data mining is decision rules and tree without which data mining is incomplete. These methods are being developed for pattern recognition and for statistic learning. Whereas data analysis is the use of different analytical tools and methods to convert the collected data into a useful form. It helps to develop solutions and verify the hypothesis. Online Analytical Processing (OLAP) becomes very popular data analysis tool which helps the companies to prepare useful and accurate repor ts from the stored data. OLAP provides servers which organise the data of the companies into multidimensional hierarchies, called Cubes which enables to companies to analyse their data with high-speed. OLAP provides the information into a graphical and tabular form which can be manipulated extensively (Management Association, 2015). Cut throat competition and global presence of the companies paced the popularity of data analysis and data mining tools. Data analysis tools are like a well of knowledge for the businesses which stores the all the information and make them available for the managers. It also helps to make prompt and profitable decisions (Wiley Ben, 2017). Data mining and analysis have become an important sunset of business intelligence which also helps in database management and warehousing. Information technology has brought revolution in the business as manual recoding of transitions has now become obsolete and companies have made business process fully automated. Successful operations of global companies are due to their data mining and analysis tools. Data analysis tools are widely used to analyse the pattern of customer behaviour. Collected data are used to analyse the to discover the movement and buying behaviour of the customers which helps the businesses to predict future demand of the custo mers for products or services. In order to deal with the mountains of data, businesses store data from various sources and summarise such data in a standard reporting form. Data are gathered from both internal and external sources. Data mining enables to arrange these data into more organised and accurate form to prepare reports which are used by the management to take business decisions related to its products or services, operations and buying behaviours of its customers (Williams, 2011). Organisations need tools to extract useful information from the stored data. Data analysis tools help to bring the stored data into a single format. Raw and stored data are converted into useful formats with the help of data analysis tool. Data analysis includes simplifying, classification and reporting the data into a standard format (Maimon Rokach, 2010). Data mining and analysis facilitate the businesses to synchronise with the current market demand and customers needs. It also suggests scope for improvement to the management and takes appropriate decisions. Data analysis and mining are a qualitative technique to evaluate the business performance. Decision-making process now becomes convenient with the help of data analysis tool. Success or failure of a company is fully depending on the decision making of its management (Dubitzky, 2008). Ethical Implications Around Gathering, Storing and using Customer Information Ethics is an integral part of every business as it ensures the honesty and commitment of business towards its customers. Ethics guarantees the quality, freshness and originality of gathered data so that the decisions based on such data proves more profitable and feasible for both company and its customers. Followings are the ethics of gathering data: Minimal Risk: This is very important aspect of data gathering which ensures that the collected data will not expose any harm to anyone mentally, physically and emotionally. Informed Consent: Participation must be from free consent. One should not be forced or coerced to participate in the data collection or provide any kind of information. Participants must be informed about the usage of their given information before taking their responses. It is the duty of the researcher to acknowledge the queries of the respondents and disclose them about the usage of their responses. Consent of participants must be of free will. Informed consent maintains the integrity and reliability of gathered data (Maimon Rokach, 2010). Anonymity and Confidentially: Both the words have different meanings are often interchangeable. Anonymity refers to no identification of respondents on the basis of their submitted responses. It encourages the participants to being honesty in their responses. However, follow-up is very difficult as the identity of respondents cannot be disclosed. While conducting observations, or focus group surveys anonymity is not useful. Whereas confidentiality means that the respondents identity is known to the investigator but it is expected that he/she will not disclose it to anyone. While reporting qualitative data, the identity of respondents must be deleted or disguised. Data Security: it is an essential part of data gathering. Once gathered data or information must be protected and stored in the reliable source. An electronic device can be used as they are password protected and has limited access. Compensation: A reasonable compensation must be delivered to the participants for their valuable participation. Compensation must be based upon the time and efforts and risk of the participants. If there is no compensation is to be paid than the participants must be communicated prior to their participation and not forced to provide their valuable suggestion for free, until they are not ready. Amount of compensation must be communicated clearly to the participants and must be fair enough to reward their contribution (Management Association, 2015). There are eight principles of data protection which are as follows: Data must be processed fairly and lawfully. Provided data is adequate, relevant and not excessive. Gathered data is available for limited purposes and usage. Data is accurate and does not contain any biases. Data has been processed in accordance with the law. Data is not kept for longer or unnecessary time. Data is not transferred to another country without adequate protection. Data must be present in secure mode. Excessive use of technology created many threats for the companies and increased the rate of cyber crime. Valuable and confidential data get hacked which causes huge losses to the companies. Although technology made the work so easy and convenient but it also carries consequences with it. Customer information is very important data for every company as most of the decisions are being taken on the basis of this information so it is essential for the companies to keep this data confidential and secret especially from its competitors. Customer information should not be used unethically by any company and they should ensure the integrity and confidentially of such information (Kantardzic, 2011). References Wang, J. 2008. Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications. London: IGI Global. Dubitzky, W. 2008. Data Mining in Grid Computing Environments. Oxford: John Wiley Sons. Kantardzic, M. 2011. Data Mining: Concepts, Models, Methods, and Algorithms. New Jersey: John Wiley Sons. Wiley, A., Ben, O.H. 2017. CCSP (ISC)2 Certified Cloud Security Professional Official. New York: John Wiley Sons. Maimon, O. Rokach, L. 2010. Data Mining and Knowledge Discovery Handbook. London: Springer Science Business Media. Management Association. 2015. Business Intelligence: Concepts, Methodologies, Tools, and Applications: Concepts, Methodologies, Tools, and Applications. Hershey: IGI Global. Williams, G. 2011. Data Mining with Rattle and R: The Art of Excavating Data. London: Springer Science Business Media.