Socio-Economic Conditions of Home-Based Working Women: A Qualitative Study in Hyderabad, Sindh

This research paper focuses on socio-economic conditions of home-based working women in Hyderabad Division, of Sindh Pakistan. Main objectives of this research are (i) to analyze the Socio-economic condition of home-based working women (ii) to assess the poverty and home-based work (iii) to find out the illiteracy and home-based work (iv) to investigate the role of handicrafts and home-based work in cultural and economic development (v) to unearth the Sindhi culture of handicrafts in Hyderabad Division. To achieve research objectives qualitative research approach is adopted and data is collected by four case studies in Hyderabad division. All cases are selected randomly and analyzed by using thematic analysis method. Present study concluded that researched area is rich in handicrafts business. Women engaged themselves in home-based work due to poverty, unemployment and poor financial conditions of their families. This business has very low profit but female preferred this work due less skills and education required to carry handicrafts business. Home-based workers felt empowered due to having their own income and took part in decision making. In last it is recommended for policy makers and government agencies to give priority to this business because it has potential. It is necessary for economic development of families, culture and country.

Efficient Spectral Analysis of Time Series

This work focuses on efficient, joint time-frequency analysis of time series data. Joint time-frequency analysis is based on the sliding window. There are two major contributions of this thesis. Firstly, we haveThis work focuses on efficient, joint time-frequency analysis of time series data. Joint time-frequency analysis is based on the sliding window. There are two major contributions of this thesis. Firstly, we have introduced a notion of “aggregate spectrogram (AS)” which is a unimodal distribution at each time instant. The AS is extremely useful and computationally efficient when we are interested in a few spectral features and not the entire spectrum. Properties/characteristics of the AS have been listed. A para- metric method, based on a second order autoregressive model of the signal, for the construction of the AS, has been described. Of all the existing spectral estimation tools, the AS has the least computational complexity. Based on the AS, instan- taneous frequency estimation for multicomponent signals with equal amplitudes has been achieved. The AS does not require Goertzel filters in dual tone multi frequency detection applications. The AS finds many potential application. A few examples are voice activity detection, edge detection, motion vector estimation etc. Secondly, the problem of estimating the instantaneous frequency and band- width for multicomponent signals with time varying amplitudes has been solved by employing a new peak detection algorithm. The algorithm has been shown to outperform existing algorithms when the frequencies and amplitudes of the multi- component noisy signals are time-varying. Other contributions of the thesis include: low computational cost algorithms for the sliding discrete Fourier transform, and algorithms for its extension to spectral interpolation through zero padding and window padding. A low cost, optimized iii split-radix FFT architecture for zero-padded signals is also proposed. The Wiener-Khintchine theorem (WKT) yields better spectral estimates of Gaussian signals as compared to the discrete Fourier transform (DFT). Higher order spectra find utility in case of additive colored noise or the signals are non- Gaussian. Due to high computational complexities, the WKT and higher order spectra are avoided in the sliding window based spectral analysis. We have devel- oped recursive forms of the WKT, bispectrum and trispectrum whose computa- tional complexities have reduced to linear, quadratic and cubic orders, respectively introduced a notion of “aggregate spectrogram (AS)” which is a unimodal distribution at each time instant. The AS is extremely useful and computationally efficient when we are interested in a few spectral features and not the entire spectrum. Properties/characteristics of the AS have been listed. A para- metric method, based on a second order autoregressive model of the signal, for the construction of the AS, has been described. Of all the existing spectral estimation tools, the AS has the least computational complexity. Based on the AS, instan- taneous frequency estimation for multicomponent signals with equal amplitudes has been achieved. The AS does not require Goertzel filters in dual tone multi frequency detection applications. The AS finds many potential application. A few examples are voice activity detection, edge detection, motion vector estimation etc. Secondly, the problem of estimating the instantaneous frequency and band- width for multicomponent signals with time varying amplitudes has been solved by employing a new peak detection algorithm. The algorithm has been shown to outperform existing algorithms when the frequencies and amplitudes of the multi- component noisy signals are time-varying. Other contributions of the thesis include: low computational cost algorithms for the sliding discrete Fourier transform, and algorithms for its extension to spectral interpolation through zero padding and window padding. A low cost, optimized split-radix FFT architecture for zero-padded signals is also proposed. The Wiener-Khintchine theorem (WKT) yields better spectral estimates of Gaussian signals as compared to the discrete Fourier transform (DFT). Higher order spectra find utility in case of additive colored noise or the signals are non- Gaussian. Due to high computational complexities, the WKT and higher order spectra are avoided in the sliding window based spectral analysis. We have devel- oped recursive forms of the WKT, bispectrum and trispectrum whose computa- tional complexities have reduced to linear, quadratic and cubic orders, respectively