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"This work investigates two-dimensional change detection methods. The existing methods in the literature are grouped into four categories: pixel-based, transformation-based, texture analysis-based, and structure-based. In addition to testing existing methods, four new change detection methods are introduced: fuzzy logic-based, shadow detection-based, local feature-based, and bipartite graph matching-based. "

"Two multi-temporal as well as multi-sensor data of LANDSAT MSS (1979) and MOS (1991) were used for Mangrove Forest change detection in Tg. Karawang, West Java. About 13,986 hectare decrease of total volume of the Mangrove Forest were detected for 12 years from 1979 to 1991. Procedures for change detection of vegetated areas employed in this study were detected by post-classification differencing technique. Landsat MSS image was registered to MOS image after the extraction of the test area from the each original image. A land cover classification image of the Landsat MSS in 1979 was produced by the supervised maximum likelihood classification method, another land cover classification image of the MOS Data 1991 was produced by the same method."

"Updating mechanism is the most important thing in giving the information for the end-user by send-ing the data from client to the server. There are several kinds of update mechanisms, one of them is reporting protocol. Reporting protocol sends the data from the client to the server continuously within a certain time period. Sometimes, the data that is sent continuously from the client to the server gives the same information repeatedly to the end-user. Although, there is no need for same information to be sent to the end-user repeatedly. This can cause a large amount of bandwidth usage. In this research, the researcher developed an improvement of reporting protocol mechanism for mobile user using cha-nge detection and resource-aware data sensing to minimize the bandwidth and resource usage. The data transmission frequency is reduced by the user activity changes prediction and the data sensing speed is reduced by adaptive data sensing. The results show that the improvement of reporting protocol mechanism adaptively can improve reporting protocol performance. This is shown by the improvement of the bandwidth efficiency up to 36-97%, memory efficiency at 1.5-6% and battery efficiency at 7-13%.Mekanisme update memegang peranan penting dalam menyampaikan informasi kepada end-user dengan melakukan pengiriman data dari klien ke server. Ada beberapa mekanisme update yang digunakan, salah satunya adalah reporting protocol. Reporting protocol mengirimkan data dari klien ke server secara kontinyu dalam interval waktu tertentu dimana terkadang memberikan informasi yang selalu sama dan berulang kepada end-user. Padahal, informasi yang sama tidak perlu dikirim secara berulang kepada end-user karena menyebabkan penggunaan bandwidth menjadi kurang efisien. Dalam penelitian ini, peneliti mengembangkan sebuah perbaikan mekanisme reporting protocol dengan change detection dan resource aware data sensing untuk menghemat penggunaan bandwidth dan resource. Mekanisme perbaikan reporting protocol yang dilakukan adalah mengurangi frekuensi pengiriman data dengan memprediksi adanya perubahan aktivitas dan posisi pada user. Prediksi perubahan aktivitas dan posisi digunakan sebagai trigger ketika akan melakukan pengiriman data. Hasil penelitian menunjukkan bahwa mekanisme reporting protocol secara adaptif dapat meningkatkan performa reporting protocol. Hal ini ditunjukkan dengan penghematan bandwidth sebesar 36-97%, penghematan memori sebesar 1.5-6% dan penghematan baterai sebesar 7-13%."

"The paper concerns mathematical modeling of free surface open-channel wafer flow.Two-dimensional vertical Reynolds-averaged Navier-Stokes equations were used to simulate the flow....."

"Tujuan utama dari deteksi perubahan (change detection) adalah untuk mendeteksi perbedaan pada dua citra satelit di wilayah yang sama tetapi diambil pada waktu yang berbeda. Deteksi perubahan merupakan salah satu masalah penginderaan jauh yang cukup sulit karena perubahan yang ingin dideteksi (real-change) bercampur dengan perubahan semu (pseudo-change). Hal ini terjadi akibat adanya faktor perbedaan kecerahan, perbedaan kelembapan, perbedaan musim dan lain-lain yang terdapat pada kedua citra. Kemunculan Vision Transformer (ViT) sebagai model state-of-the-art pada berbagai permasalahan di Computer Vision turut serta menggeser peran Convolution Neural Network (CNN) di bidang deteksi perubahan. Walaupun ViT mampu menangkap interaksi jarak jauh long-range attention dari setiap patch citra, akan tetapi kompleksitas komputasinya meningkat secara kuadratik terhadap jumlah patch. Salah satu solusi untuk mengurangi kompleksitas komputasi pada ViT adalah dengan mereduksi matriks Key (K) dan Values (V) pada mekanisme Self-Attention (SA). Akan tetapi, reduksi tersebut menurunkan efektivitas ViT akibat adanya informasi yang hilang, sehingga terjadi trade-off antara efektivitas dan efisiensi pada metode deteksi perubahan. Untuk mengatasi masalah tersebut, penulis mengembangkan metode deteksi perubahan baru bernama WaveCD dengan memanfaatkan dekomposisi Discrete Wavelet Transform (DWT) untuk mereduksi matriks K dan V. Selain berfungsi untuk mereduksi data, dekomposisi DWT juga berfungsi untuk mengekstraksi fitur-fitur yang penting yang mewakili citra sehingga data awal dapat diaproksimasi melalui proses Inverse Discrete Wavelet Transform (IDWT). Pada dataset CDD, hasil evaluasi WaveCD mencapai kenaikan nilai IoU sebesar 14.7% dan F1-score sebesar 8% terhadap nilai evaluasi metode pembanding, SwinSUNet. Sedangkan pada dataset LEVIR-CD, hasil evaluasi WaveCD mencapai kenaikan nilai IoU sebesar 4% dan F1-score sebesar 2%

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Change detection is a remote sensing task for detecting a change from two satellite imagery in the same area while being taken at different times. Change detection is one of the most difficult remote sensing tasks because the change to be detected (real-change) is mixed with apparent changes (pseudo-change) due to differences in the two images, such as brightness, humidity, seasonal differences, etc. The emergence of a Vision Transformer (ViT) as a new standard in Computer Vision, replacing Convolutional Neural Network (CNN), also shifts the role of CNN in the field of DP. Although ViT can capture long-range interactions between image patches, its computational complexity increases the number of patches quadratically. One solution to reduce the computational complexity in ViT is to reduce the Key (K) and Values (V) matrices in the Self-Attention(SA) mechanism. However, this reduction also reduces the effectiveness of ViT due to missing information, resulting in a trade-off between the effectiveness and efficiency of the method. To solve the problem, we developed a new change detection method called WaveCD. WaveCD uses Wave Attention (WA) instead of SA. WA uses the Discrete Wavelet Transform (DWT) decomposition to reduce the K and V matrices. Besides reducing the data, DWT decomposition also serves to extract important features that represent images so that the initial data can be approximated through the Inverse Discrete Wavelet Transform (IDWT) process. On the CDD dataset, WaveCD outperforms the stateof-the-art CD method, SwinSUNet, by 14.7% on IoU and 8% on F1-score. While on the LEVIR-CD dataset, WaveCD outperforms SwinSUNet by 4% on IoU and 2% on F1-score."

"This book combines theoretical concepts with experimental results on thermal microwave radiation to increase the understanding of the complex nature of terrestrial media. With the emphasis on radiative transfer models, this new book covers the urgent needs for the transition from the experimental phase of microwave remote sensing to operational applications. This book covers all terrestrial aspects from clear to cloudy atmosphere, precipitation, ocean and land surfaces, vegetation, snow and ice.A chapter on new results of microwave dielectric properties of natural media, covering wavelengths from the decimetre to the submillimetre range, will be a source for further radiative transfer developments, which extends the applicability to radar and other electromagnetic tools, which include extraterrestrial objects, for example planets and comets."

"ABSTRACT

Resistivity surveying is commonly interpreted in term of one dimensional approach, which is based on the assumption that the subsurface consists of a system of parallel, horizontal, homogeneous and isotropic layers. Very often this assumption amounts to a gross simplification of the actual geology. Consequently, 1D-interpretation can not overcome equivalence problems, lateral and vertical in-homogeneity, as well as it can not determine more realistic subsurface structures. However, if we use a two-dimensional approach by making provision for the variation of resistivity in two directions, x and z direction or y and z direction (two-dimensional interpretation), and by assuming that no resistivity variation along the third orientation (i.e. y or x axes), a more realistic model of the subsurface can be constructed.

Fundamental equations, finite-difference equations, numerical algorithm and modification of Fortran computer codes, based on Dey's codes, are described in this Masters Thesis.

The simulations of 2D-resistivity modelling for equivalence problems, lateral and vertical in-homogeneity, as well as case study of 2D-resistivity modelling in determining resistivity structures in Ulubelu Geothermal Prospect, Indonesia, are also presented. It has shown that the limitations of 1D-interpretation can be overcome by using the two-dimensional approach."