Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an

The electric energy is one of the most important renewable energies used in the world as it is the main source for sustainable development and economic development through its use in (production, transport and distribution), and in Iraq, the electric power sector has suffered from many problems and obstacles, as providing electric current is one of the most prominent difficulties and challenges That successive governments and residents have faced since the early nineties of the last century and are still ongoing, and that Iraq has all the climatic conditions for developing the work of the electricity system from renewable energies such as solar and hydroelectric energy, as well as gas fields that have become a Basic pillar of pow

In this paper different channel coding and interleaving schemes in DS/CDMA system over multipath fading channel were used. Two types of serially concatenated coding were presented. The first one composed of Reed-Solomon as outer code, convolutional code as inner code and the interleaver between the outer and inner codes and the second consist of convolutional code as outer code, interleaved in the middle and differential code as an inner code. Bit error rate performance of different schemes in multipath fading channel was analyzed and compared. Rack receiver was used in DS/CDMA receiver to combine multipath components in order to enhance the signal to noise ratio at the receiver.

Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution than general-purpose microprocessors by taking advantage of reusable modules, parallel processes and specialized computational components. Modern high-density Field Programmable Gate Arrays (FPGAs) offer the required flexibility and fast design-to-implementation time with the possibility of exploiting highly parallel computations like those required by ANNs in hardware. The bounded width of the data in FPGA ANNs will add an additional error to the result of the output. This paper derives the equations of the additional error value that generate from bounded width of the data and proposed a method to reduce the effect of the error to give

In this paper, the error distribution function is estimated for the single index model by the empirical distribution function and the kernel distribution function. Refined minimum average variance estimation (RMAVE) method is used for estimating single index model. We use simulation experiments to compare the two estimation methods for error distribution function with different sample sizes, the results show that the kernel distribution function is better than the empirical distribution function.