Gender discrimination in East Asia Essay Example | Topics and Well Written Essays - 1500 words

Gender discrimination in East Asia - Essay Example Burying a newborn child because that is female, limiting the access of women to education, work and health, burying the wife of a person who dies are some common examples of gender discrimination in East Asia. Violence against women leads to robbing the aspiration and self-esteem of women which possess life-long psychological cost. In East Asia, female’s admission in primary school is as lower as 26% than male students. Such statistics are also observed in health sector. The ratio of female to male in population is worsening in areas like Central and North Asia, South Asia and Countries of Pacific Islands. In many East Asian countries, out of every 10 girls, one dies due to limited access to health and out of 50, one woman dies during delivery and pregnancy. Gender discrimination not only brings problems for female population of a country but also affects the social and economic development of a country. There is huge economic and social cost behind the ramifications of gender discrimination. Blocking the access of females to health and education is not only unfavorable for economic growth and social welfare but also becomes obstacle in the path of labor force participation and human capital development. Women’s silence and inability to practice their civil rights deprive them from having social participation and this phenomenon also obstructs economic growth. Thesis Statement This paper postulates that governments of East Asian countries must take step forward to eradicate the employment gender inequalities in all sectors of the economy. Point of Support 1- Huge Social and Economic Cost There is huge economic and social cost behind gender discrimination. Discrimination hinders women’s participation in social and economic activities, reduces productivity and distracts resources. According to a recent survey, higher female participation and increased employment among women increases the output and growth of that region as shown in the mentio ned table. Source: (World Bank, World Development Indicators Washington D.C., 2006) The above table reveals that from 1990 to 2004, the greatest effect has been on Malaysia, Indonesia and India, where the participation of female labor is lowest of all. These estimations reflect the opportunity cost that has been incurred on gender discrimination. The concept of working women is very common in the economy of United States, and the growth of United States is thereby higher than all the other mentioned countries. Lack of participation of women in political, legal, economic, social and other activities hinder the growth of economy. In a country where both male and female population is working, it seems to grow more profoundly than the country where only male population is working. Let us take an example of a family. A family where both parents are working will generate double income, but a family where only father is working, incurring expenditures of the entire family and meeting expen ses from single income, it will be difficult to recover all the expenses. Point of Support 2- Psychological Cost Gender discrimination poses life-long threats to women’s expertise, self-esteem and capabilities. It not only restricts their opportunities but also spoils aspirations of women. It sabotages their mentality of building self-direction and competence. The unnecessary restrictions which are imposed on women make them clinically depressive and produce a state of helplessness. Such factors compel women to contribute into global burden of illness. Another life-long resentment is that of an unwanted pregnancy which is then transferred to the child. In regions where there is not any

Signal Processing Research Paper Example | Topics and Well Written Essays - 1000 words

Signal Processing - Research Paper Example One of these digital signal processing techniques is adaptive filtering. Adaptive Filters Haykin (2006) defines an adaptive filter as a system which is self-designing and reliant on a recursive algorithm for its operation. This feature enables an adaptive to satisfactorily perform in an environment where there is scarce or no knowledge of the applicable statistics. Diniz & Netto (2002) observe that an adaptive filter is used when either the fixed specifications are not known, or these specifications cannot be met by filters which are time-invariant. Adaptive filter’s characteristics depend on the input signal and such filters are time-varying because their parameters continually change so as to satisfy a performance requirement. The two main groups of adaptive filters are linear and nonlinear. According to Stearns & Widrow (1985), linear adaptive filters calculate an approximation of the desired response by utilizing a linear permutation of the available group of observables t hat are applied to the filter’s input. Nonlinear adaptive filters are those that depend on the input signal and their parameters change continually. Also, adaptive filters can be classified as supervised and unsupervised adaptive filters. Supervised adaptive filters apply the presence of a training series that gives different outputs of a desired ouput for a particular input signal. The response that is desired is compared against the real output due to the input signal, and the error signal that results is used in adjusting the filter’s free parameters. Unsupervised adaptive filters perform alterations of their free parameters without the requirement for a desired response. Such filters are designed with a group of rules that enable it to calculate the input-output mapping with particular desirable properties (Sayed, 2003). Adaptive Filtering System Configuration Drumright (1998) establishes 4 major types of adaptive filtering configurations. These include adaptive no ise cancellation, adaptive inverse system, adaptive system identification and adaptive linear prediction. Algorithm implementation in all these systems, but the configuration is different. They all have the same general characteristics which include: an input signal x(n), a desired result d(n), an output signal y(n), an adaptive transfer function w(n) and an error signal e(n). e(n)=d(n)-y(n) The adaptive system identification determines a discrete approximation of the transfer function for an unknown analog or digital system. A similar input x(n) is applied to both the unknown system and the adaptive filter and the outputs are compared. The y(n) of the adaptive filter is subtracted from that of the unknown resulting in an error signal e(n) which is used to manipulate the filter coefficients of the adaptive system. In the adaptive noise cancellation configuration, an input x(n) and a noise source N1(n) are compared with a desired signal d(n) which comprises of a signal s(n) corrupted by another noise N0(n). The adaptive filter coefficients adapt to cause the error signal to be a noiseless version of the signal s(n). The adaptive linear prediction configuration performs two operations; linear prediction and noise cancellation. Finally, the adaptive inverse system models the inverse of the unknown system u(n), an aspect which is useful in adaptive equalization (Drumright, 1998). Conclusion Just as discussed above, the classical applications of adaptive filt