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"penelitian ini diusulkan implementasi 2D dan 3D Set Partitioning In Hierarchical Trees (SPIHT) coding pada kompresi data ECG multi-lead. Implementasi SPIHT mereduksi tiga jenis redundansi yang umumnya terdapat pada sinyal electrocardiogram (ECG), yaitu redundansi intra-beat, inter-beat, dan inter-lead. Kami juga mengusulkan tiga teknik optimisasi untuk meningkatkan kinerja kompresi lebih lanjut dengan mengelompokkan sinyal ECG yang berasal dari beberapa lead, menyusun kembali posisi ECG cycle pada 2D ECG array (beat reordering), dan menormalisasikan amplitudo dari 2D ECG array dengan residual calculation. Beat reordering menyusun posisi beat pada 2D ECG array berdasarkan kemiripannya dengan beat terdekat. Penyusunan ini mengurangi variasi antar beat-beat yang berdekatan sehingga 2D ECG array mengandung lebih sedikit komponen frekuensi tinggi. Residual calculation mengoptimalkan penggunaan ruang penyimpanan lebih lanjut dengan meminimalkan variasi amplitudo dari 2D ECG array. Hasil eksperimen terhadap sejumlah record pada St Petersburg INCART 12-lead Arrhythmia Database menunjukkan bahwa metode yang diusulkan menghasilkan distorsi rendah pada rasio kompresi 8 dan 16. Hasil eksperimen juga memperlihatkan bahwa pendekatan 3D SPIHT memiliki kinerja kompresi yang lebih baik dibanding 2D SPIHT. Untuk mengevaluasi kualitas sinyal hasil rekonstruksi pada permasalahan klasifikasi, pada penelitian ini kinerja dari metode kompresi sinyal ECG dianalisis dengan cara membandingkan sinyal asli dengan sinyal hasil rekonstruksi pada dua permasalahan; pertama, klasifikasi sleep stage berdasarkan sinyal ECG; kedua, klasifikasi arrhythmia. Hasil eksperimen menunjukkan bahwa akurasi dari klasifikasi sleep stage dan klasifikasi arrhythmia menggunakan sinyal hasil rekonstruksi sebanding dengan menggunakan sinyal input. Metode yang diusulkan dapat mempertahankan karakteristik sinyal pada kedua permasalahan klasifikasi tersebut.

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In this study we proposed the implementation of 2D and 3D Set Partitioning In Hierarchical Trees (SPIHT) coding to a multi-lead ECG signal compression. The implementation of SPIHT coding decorrelates three types of redundancy that typically found on a multi-lead electrocardiogram (ECG) signal i.e. intra-beat, inter-beat, and inter-lead redundancies. We also proposed three optimization techniques to improve the compression performance further by grouping the ECG signal from precordial and limb leads, reordering the ECG cycles position in the 2D ECG array, and normalizing the amplitude of 2D ECG array by residual calculation. Beat reordering rearranged beat order in 2D ECG array based on the similarity between adjacent beats. This rearrangement reduces variances between adjacent beats so that the 2D ECG array contains less high frequency component. The residual calculation optimizes required storage usage further by minimizing amplitude variance of 2D ECG array.

Our experiments on selected records from St Petersburg INCART 12-lead Arrhythmia Database show that proposed method gives relatively low distortion at compression rate 8 and 16. The experimental results shows that 3D SPIHT approach gives better compression performance than 2D SPIHT. To evaluate the quality of reconstructed signal for classification task, we analyzed the performance of electrocardiogram (ECG) signal compression by comparing original and reconstructed signal on two problems. First, automatic sleep stage classification based on ECG signal; second, arrhythmia classification. Our experimental results showed that the accuracy of sleep stage classification and arrhythmia classification using reconstructed ECG signal from the proposed method is comparable to the original signal. The proposed method preserved signal characteristics for the automatic sleep stage and arrhythmia classification problems."

"ABSTRAKLossles data compression erat kaitannya engan masalah pengkodean. Bahkan inti dari kompresi data itu sendiri adalah pengkodean. Berbagai manipulasi data telah ditemukan oleh para ilmuwan di bidang lossless data compression. Salah satunya dan yang paling menarik adalah Burrows- Wheeler Transform (BWT) yang diambil dari nama dua penemuanya yaitu Michael Burrows dan David Wheeler. Hasil dari transformasi ini sangat bagus untuk proses pengkodean pada kompresi data.Skripsi ini mencoba merancang suatu kompresi data yang berbasis BWT dengan mempergunakan pengkodean konvensional Run Length Encoding, Move to Front Transform, dan Arithmetic Coding, kemudian coba diuji dan dianalisa performanya dibandingkan dengan standar program kompresi Linux gzip 1.3.3 agar dapat melihat kegunaan BWT tersebut."

"Data is of high importance especially for large companies. When data is abundant, some companies thus require a good software or an effective tool to compress the data. Data compression is the process of converting an input data stream into another data stream that has a smaller size. Data compression software is very useful because it can reduce the size of the data itself. The main idea of the LZW encoding is to identify the longest pattern for each accumulated segment of the source text, and encode them by the indices in the dictionary. If no match is found in the dictionary, the segment will become a new entry to the dictionary. There will be a match found in the dictionary if the same segment is seen next time. We put another check on the segment by adding a second index on a dictionary that represents a reversed sequence of characters. The experiment was conducted on text file with the size from about a 3,000 up to 60,000 Bytes and a code with length of bits from 9 to 16 bits. The results show that our proposed method gave fewer (better) compression ratio (16%, on average) compared to the standard LZW (56%) and LZW++ (36%)"

"Teknik pemampatan (compression) adalah salah satu cara untuk mereduksi pemakaian Iebar pita (bandwidth). Pada aplikasi transmisi ataupun penyimpanan gambar, teknik ini merupakan suatu cara untuk mereduksi pemakaian lebar pita dengan cara mengurangi jumlah bit yang merepresentasikan gambar. Salah satu teknik pemampatan gambar adalah dengan menggunakan metode pengalian nilai koefisien subband detail, setelah gambar yang akan dimampatkan ditransformasi wavelet. Teknik pemampatan ini cukup efektif karena selain memiliki rasio kompresi yang besar, nilai PSNR gambar setelah diekstrak kembali culcup baik. Disamping itu waktu proses pemampatan tidak terlalu lama. Suatu gambar setelah ditransformasi wavelet maju akan menghasilkan 2 (dua) jenis subband yaitu lowpass residue dan subband detail (subband horisontal detail, subband vertikal detail dan subband diagonal detail). Metoda ini bekerja dengan cara mengalikan subband detail dengan suatu konstanta (konstanta pengali subband detail) sehingga menghasilkan entropi yang lebih kecil dari gambar asli. Skripsi ini akan menjelaskan kompresi gambar dengan metoda tersebut dengan mensimulasikan pada komputer. Hasil simulasi berupa perhitungan-perhitungan yang akan dianalisa sehingga diperoleh jenis filter wavelet dan kombinasi konstanta pengali subband detail yang optimum."

"Digital video compression has revolutionised the broadcast industry. Its implementation has been the vital key to the expansion of video via satellite, cable, internet and terrestrial TV. However, new technologies not only enable new applications, they also create new challenges such as how to measure video quality, and how to maintain video quality in concatenated compression systems.Video Compression Systems provides an overview on many issues concerning today's complex digital video systems: from video quality measurements to statistical multiplexing, from pre-processing to transcoding and concatenation. It explains video compression systems from first principles and gives a detailed summary of currently used MPEG standards, as well as non-MPEG algorithms. Furthermore, it provides a summary of motion estimation algorithms and explains processing priorities for mobile applications, HDTV, contribution and distribution systems, as well as for end user systems.Video Compression Systems focuses intentionally on the principles rather than the mathematics in order to make it more readable and accessible to a wider audience. It is aimed at senior undergraduate students taking modules in video technologies, multimedia processing or video compression, as well as television engineers working on video compression systems."

"Managing data in motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment.The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired.The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing data in motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects."