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"In 2011, massive flooding and inundation in the Chao Phraya River basin, in Thailand, causedserious damage to various activities for a prolonged period of time. Although snapshot images ofthe inundated area are available, detailed information including temporal changes of the inundatedareas and the relationship with meteorological and hydrological conditions are not welldocumented, particularly for the middle and upper sections of the basin. Therefore, we conductedan analysis using two types of satellite data, HJ-1A and Envisat, to better understand behavior ofthe large-scale inundation occurred in 2011, focusing on the middle section of the Chao PhrayaRiver basin. In the analysis, water surface in selected domains was extracted using the NDWI valuecalculated from HJ-1A data. The threshold value of the Envisat ASAR image was then adjusted sothat the inundated area estimated from Envisat gives the closest possible match with that estimatedfrom HJ-1A. Finally, the inundated area was estimated for the whole study domain based on thesame threshold value from the Envisat data. Results indicated that the inundated area began toextend along the Yom and Nan rivers in early August and continued to spread down to the NakhonSawan city area until October. A significant increase in inundated areas occurred between Sep. 2and Sep. l3, during which higher rainfall intensity was observed. Even after the water level inrivers receded below the bank-full elevation, large areas were left inundated along rivers,particularly over lowlying marsh and paddy fields. In addition, several areas located far fromrivers were also inundated, which was likely a consequence of water ponding in paddy fields."

"Channel estimation memiliki definisi untuk memperkirakan koefisien filter melalui sinyal yang diterima dan informasi lain yang dikenal. Ada beberapa metode channel estimation yang dapat diguanakan seperti minimum mean square error, least square, zero-force, maximum likelihood. Dalam penelitian ini, least square dan zero-force akan dibahas lebih lanjut. Kedua jenis ini dikenal umum sebagai jenis yang paling sederhana dan paling mudah untuk diterapkan di dalam komunikasi nirkabel. Meskipun metode lain seperti minimum mean square error atau maximum likelihood telah dikenal secara luas untuk memperkirakan kanal, metode tersebut terbukti sangat kompleks untuk diimplementasikan.Penelitian ini bertujuan untuk memahami efek dan membandingkan kinerja channel estimation dengan menggunakan kapasitas multi antenna seperti Single-Input Single Output (SISO), Multiple-Input Single-Output (MISO), Single-Input Multiple-Output (SIMO) dan Multiple-Input Multiple-Output (MIMO). Least square dan metode estimasi zero-force telah digunakan dengan memakai modulasi Binary Phase Shift Keying (BPSK), hal ini dikarenakan BPSK adalah metode phase shift keying yang paling sederhana untuk di implementasikan di dalam penelitian ini. Pertama-tama, segala parameter perlu dikonfigurasi terlebih dahulu untuk digunakan di dalam simulasi. Kemudian, data dalam sistem pilot dikirim ke penerima menggunakan modulasi BPSK di setiap sistem multi antenna dengan menggunakan saluran nirkabel yang berbeda. Lalu, Alamouti encoder dieksekusi khusus untuk sistem MIMO dan MISO. Setelah itu, least square dan zero-force diterapkan dan tingkat kesalahan bit atau dengan nama lain bit error rate (BER) dapat dicapai dengan menyamakan sinyal yang ditransmisikan. Kemudian, analisa hasil data dengan membandingkan kinerja least square dan zero-force dapat dicapai. Pada akhirnya, hasil yang telah ditemukan menunjukkan bahwa least square melakukan performa lebih baik daripada metode estimasi zero force yang berlaku pada kinerja tingkat kesalahan bit dengan sinyal terhadap noise atau dengan nama lain signal to noise ratio (SNR).

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Channel estimation has a definition to estimate the filter coefficient through received signal and other known information. There are many methods that channel estimation can be used such as minimum mean square error, least square, zero-force, maximum likelihood. In this research, least square and zero force will be discussed further. These two types are commonly known as the simplest and easiest type to implement in the wireless communication. Even though other methods like minimum mean square error or maximum likelihood have been widely known to estimate channel, such methods are proven very complex to implement.

The aimed of this research is to understand the effect and compare the performance of the channel estimation method with the capacity of multiple antennas such as Single-Input Single-Output (SISO), Multiple-Input Single-Output (MISO), Single-Input Multiple-Output (SIMO) and Multiple-Input Multiple-Output (MIMO). Least square and zero force estimation method were being used with BPSK modulation, this is due to BPSK is the simplest phase shift keying.

First, the parameters need to be configured in simulation software. Then, the data within pilots system transmitted to receiver using BPSK modulation in each of the wireless antenna systems with different wireless channels. Space time block coding of Alamouti encoder were executed for MIMO and MISO system. After that, least square and zero force method were implemented and bit error rate (BER) performance can be achieved by equalizing the transmitted signal. Analysing the result by comparing the performance of least square and zero force were accomplished.

In the recent result, it was shown that the least square performs better than zero force estimation method for the performance of bit error rate with signal to noise ratio."

"The objective of this study is to determine the solar energy potential in Indonesia using artificial neural networks (ANNs) method. In this study, the meteorological data during 2005 to 2009 collected from 3 cities (Jakarta, Manado, Bengkulu) are used for training the neural networks and the data from 1 city (Makasar) is used for testing the estimated values. The testing data were not used in the training of the network in order to give an indication of the performance of the system at unknown locations. Fifteen combinations of ANN models were developed and evaluated. The multi layer perceptron ANNs model, with 7 inputs variables (average temperature, average relative humidity, average sunshine duration, longitude, latitude, latitude, month of the year) are proposed to estimate the global solar irradiation as output. To evaluate the performance of ANN models, statistical error analysis in terms of mean absolute percentage error (MAPE), mean absolute bias error (MABE) and root mean square error (RMSE) are conducted for testing data. The best results of MAPE, MABE, RMSE are found to be 7.4%, 1.10 MJ/m2 and 0.17 MJ/m2 respectively as 7 neurons were set up in the hidden layer. The result demonstrates the capability of ANN model to generate the solar irradiation estimation in Indonesia."