Compression is useful because it helps us to reduce the resources usage, such as data storage space or transmission capacity. Compression methods have a long list. In this paper , reviews of different basic lossless data and lossy compression techniques are considered. On the basis of these techniques we try to purpose a bit reduction algorithm used for compression of text data which is based on number theory system and file differential technique. It is a simple compression and decompression process which is free from time complexity.

A Survey on Different Compression Techniques and Bit Reduction Algorithm for Compression of Text/Lossless Data

Data compression is the process of reducing the size of a file to effectively reduce storage space and communication cost. The evolvement in technology and digital age has led to an unparalleled usage of digital files in this current decade. The usage of data has resulted to an increase in the amount of data being transmitted via various channels of data communication which has prompted the need to look into the current lossless data compression algorithms to check for their level of effectiveness so as to maximally reduce the bandwidth requirement in communication and transfer of data. Four lossless data compression algorithm: Lempel-Ziv Welch algorithm, Shannon-Fano algorithm, Adaptive Huffman algorithm and Run-Length encoding have been selected for implementation. The choice of these algorithms was based on their similarities, particularly in application areas. Their level of efficiency and effectiveness were evaluated using some set of predefined performance evaluation metrics namely compression ratio, compression factor, compression time, saving percentage, entropy and code efficiency. The algorithms implementation was done in the NetBeans Integrated Development Environment using Java as the programming language. Through the statistical analysis performed using Boxplot and ANOVA and comparison made on the four algorithms, Lempel Ziv Welch algorithm was the most efficient and effective based on the metrics used for evaluation. Keywords: Data compression, lossless, evaluation, entropy, algorithm

/pdf/empirical-and-statistical-evaluation-of-the-effectiveness-of-52ekhlf21g.pdf

Empirical and Statistical Evaluation of the Effectiveness of Four Lossless Data Compression Algorithms

Pixelated images are used to transmit data between computing devices that have cameras and screens. Significant compression of pixelated images has been achieved by an “edge-based transformation and entropy coding” (ETEC) algorithm recently proposed by the authors of this paper. The study of ETEC is extended in this paper with a comprehensive performance evaluation. Furthermore, a novel algorithm termed “prediction-based transformation and entropy coding” (PTEC) is proposed in this paper for pixelated images. In the first stage of the PTEC method, the image is divided hierarchically to predict the current pixel using neighboring pixels. In the second stage, the prediction errors are used to form two matrices, where one matrix contains the absolute error value and the other contains the polarity of the prediction error. Finally, entropy coding is applied to the generated matrices. This paper also compares the novel ETEC and PTEC schemes with the existing lossless compression techniques: “joint photographic experts group lossless” (JPEG-LS), “set partitioning in hierarchical trees” (SPIHT) and “differential pulse code modulation” (DPCM). Our results show that, for pixelated images, the new ETEC and PTEC algorithms provide better compression than other schemes. Results also show that PTEC has a lower compression ratio but better computation time than ETEC. Furthermore, when both compression ratio and computation time are taken into consideration, PTEC is more suitable than ETEC for compressing pixelated as well as non-pixelated images.

https://www.mdpi.com/2313-433X/4/5/64/pdf

Edge-Based and Prediction-Based Transformations for Lossless Image Compression

In the digital world, the size of images is an important challenge when dealing with the storage and transmission requirements. Compression is one of the fundamental techniques to address this problem. A number of transform based compression techniques are discussed in the literature and some are used in practice. In this paper, we propose an edge-based image transformation method which will be used with an entropy encoding technique to greatly reduce image size without loss in content. In the first stage of the proposed transform scheme, the intensity difference of neighboring pixels is calculated in the horizontal or vertical direction depending on the presence of a horizontal or vertical edge. In the second stage, the intensity differences are used to form two matrixes — one containing the absolute intensity difference and the other having the polarity of the differences. Next, Huffman or Arithmetic entropy coding is applied on the generated matrixes. The proposed edge-based transformation and entropy coding (ETEC) scheme is compared to the existing lossless compression techniques: Joint Photographic Experts Group Lossless (JPEG-LS) and Set Partitioning in Hierarchical Trees (SPIHT). Simulation results show that the proposed ETEC scheme can provide better compression compared to JPEG-LS and SPIHT algorithms for pixelated images that are used for data communication between a computer screen and a camera.

Edge-based transformation and entropy coding for lossless image compression

To reduce communication costs, to protect our data from eavesdropping and from unauthorized users, cryptographic algorithms are used. Cryptographic module has to be developed for combining the operation of compression and encryption synchronously on the file. The information file is preliminary processed and then converts into one intermediary form so that it can be compressed with better efficiency and security. In this paper an optimized approaching coding technique which deals with both the issues of size and security is introduced and characterized experimentally using the performance measurement approach java in which file of any data length can be practically compressed and encrypted using new encryption technique and a novel energy saving technique in wireless communication network with efficient hardware solution is presented. To improve the strength and capability of algorithms and to compress the transmitted data an intelligent and reversible conversion technique is applied.

Efficient Energy Saving Cryptographic Techniques with Software Solution in Wireless Network

People tend to store a lot of files inside theirs storage. When the storage nears it limit, they then try to reduce those files size to minimum by using data compression software. In this paper we propose a new algorithm for data compression, called j-bit encoding (JBE). This algorithm will manipulates each bit of data inside file to minimize the size without losing any data after decoding which is classified to lossless compression. This basic algorithm is intended to be combining with other data compression algorithms to optimize the compression ratio. The performance of this algorithm is measured by comparing combination of different data compression algorithms.

A New Algorithm for Data Compression Optimization

/pdf/a-new-algorithm-for-data-compression-optimization-3v5djk4lbs.pdf

One of the stage that performed during a maternal and fetal health monitoring is the calculation of fetal heart rate and uterine contraction using Cardiotocography (CTG). The aim of fetal health using CTG is to avoid morbidity and mortality in fetus at risk of hypoxia. This paper propose a hypoxia detection by using classification. In this study, we improve deep learning method in order to increase its capability in detecting hypoxia. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and adjusting classifier layers. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and increasing classifier layers. For whole dataset that achieved by proposed method is 81%. The improved DenseNet achieved 50%, 43%, 46% precision, recall and f1-score respectively for hypoxia class that is not achieved by standard DenseNet.

Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal

Tingkat kejahatan kasus pembobolan rumah terus meningkat, berdasarkan data yang dikumpulkan oleh Badan Pusat Statistik kasus pembobolan rumah menempati posisi kedua dengan jumlah kasus terbanyak. Perkembangan teknologi memberikan solusi dalam sebuah sistem keamanan yang lebih baik. Berbagai sistem otomatisasi telah dikembangkan, salah satunya tentang penguncian pintu, yang harus memiliki sebuah sistem keamanan yang dapat diandalkan. Penelitian yang dilakukan bertujuan untuk mengembangkan sebuah prototype kontrol kunci pintu menggunakan Raspberry Pi berbasis android yang dilengkapi dengan magnetic switch door, solenoid dan alarm. Sistem dilengkapi dengan fitur alarm dan kunci otomatis yang dapat diaktifkan melalui aplikasi android. Fitur alarm akan aktif ketika pintu terbuka pada mode terkunci, sedangkan fitur kunci otomatis akan aktif ketika pintu sudah tertutup beberapa menit. Status pintu sudah terkunci atau belum dapat dilihat pada aplikasi android, serta melakukan penguncian pintu dari jarak jauh. Hasil dari prototype yang dikembangkan yaitu dapat meningkatkan keamanan pada pintu rumah dari tindak kejahatan pembobolan pintu. 
Kata kunci: Android, Keamanan, Pintu, Raspberry Pi

/pdf/kontrol-kunci-pintu-rumah-menggunakan-raspberry-pi-berbasis-6qbgcenkic.pdf

Kontrol Kunci Pintu Rumah Menggunakan Raspberry Pi Berbasis Android

The Akebono satellite was launched in 1989 to observe the Earth’s magnetosphere and plasmasphere. Omega was a navigation system with 8 ground stations transmitter and had transmission pattern that repeats every 10 s. From 1989 to 1997, the PFX on board the Akebono satellite received signals at 10.2 kHz from these stations. Huge amounts of PFX data became valuable for studying the propagation characteristics of VLF waves in the ionosphere and plasmasphere. In this study, we introduce a method for automatic detection of Omega signals from the PFX data in a systematic way, it involves identifying a transmission station, calculating the delay time, and estimating the signal intensity. We show the reliability of the automatic detection system where we able to detect the omega signal and confirmed its propagation to the opposite hemisphere along the Earth’s magnetic field lines. For more than three years (39 months), we detected 43,734 and 111,049 signals in the magnetic and electric field, respectively, and demonstrated that the proposed method is powerful enough for the statistical analyses.

/pdf/automatic-detection-of-omega-signals-captured-by-the-3qw39bzoyx.pdf

I Made Agus Dwi Suarjaya

Papers

A New Algorithm for Data Compression Optimization

A New Algorithm for Data Compression Optimization

Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal

Kontrol Kunci Pintu Rumah Menggunakan Raspberry Pi Berbasis Android

Automatic Detection of Omega Signals Captured by the Poynting Flux Analyzer (PFX) on Board the Akebono Satellite