This thesis treats concept of autonomous temperature, relative humidity and pressure USB datalogger. Also there is explained datalogger function, hardware design with respect on device consumption and design of chassis. Furthermore, there is described communication protocol for control and reading out data by the PC. Furthermore, there are described firmware drivers for some used components and modules for USB communication, RTC and data compression. Lastly there is described software which is used for datalogger configuration and data read out.

This thesis deals with description of human hearing, audio and speech codecs, description of objective measure of quality and practical comparison of codecs. Chapter about audio codecs consists of description of lossless codec FLAC and lossy codecs MP3 and Ogg Vorbis. In chapter about speech codecs is description of linear predictive coding and G.729 and OPUS codecs. Evaluation of quality consists of description of segmental signal-to- noise ratio and perceptual evaluation of quality – WSS and PESQ. Last chapter deals with description od practical part of this thesis, that is comparison of memory and time consumption of audio codecs and perceptual evaluation of speech codecs quality.

The increasing volume of biological data requires finding new ways to save these data in genetic banks. The target of this work is design and implementation of a novel algorithm for compression of DNA sequences. The algorithm is based on aligning DNA sequences agains a reference sequence and storing only diferencies between sequence and reference model. The work contains basic prerequisities from molecular biology which are needed for understanding of algorithm details. Next aligment algorithms and common compress schemes suitable for storing of diferencies agains reference sequence are described. The work continues with a description of implementation, which is follewed by derivation of time and space complexity and comparison with common compression algorithms. Further continuation of this thesis is discussed in conclusion.

Signal compression is necessary part for ECG scanning, because of relatively big amount of data, which must be transmitted primarily wirelessly for analysis. Because of the wireless sending it is necessary to minimize the amount of data as much as possible. To minimize the amount of data, lossless or lossy compression algorithms are used. This work describes an algorithm SPITH and newly created experimental method, based on PNG, and their testing. This master’s thesis there is also a bank of ECG signals with parallel sensed accelerometer data. In the last part, modification of SPIHT algorithm, which uses accelerometer data, is described and realized.

This thesis is focused on the compression algorithm's analysis of MJPEG format and its implementation in FPGA chip. Three additional video bitstream reduction methods have been evaluated for real-time low latency applications of MJPEG format. These methods are noise filtering, inter-frame encoding and lowering video's quality. Based on this analysis, a MJPEG codec has been designed for implementation into FPGA chip XC6SLX45, from Spartan-6 family.

In the last decade, a lot of machine learning and data mining based approaches have been used in the areas of intrusion detection, malware detection and classification and also traffic analysis. In the area of malware analysis, static binary analysis techniques have become increasingly difficult with the code obfuscation methods and code packing employed when writing the malware. The behavior-based analysis techniques are being used in large malware analysis systems because of this reason. In prior art, a number of clustering and classification techniques have been used to classify the malwares into families and to also identify new malware families, from the behavior reports. In this thesis, we have analysed in detail about the use of Profile Hidden Markov models for the problem of malware classification and clustering. The advantage of building accurate models with limited examples is very helpful in early detection and modeling of malware families. The thesis also revisits the learning setting of an Intrusion Detection System that employs machine learning for identifying attacks and normal traffic. It substantiates the suitability of incremental learning setting(or stream based learning setting) for the problem of learning attack patterns in IDS, when large volume of data arrive in a stream. Related to the above problem, an elaborate survey of the IDS that use data mining and machine learning was done. Experimental evaluation and comparison show that in terms of speed and accuracy, the stream based algorithms perform very well as large volumes of data are presented for classification as attack or non-attack patterns. The possibilities for using stream algorithms in different problems in security is elucidated in conclusion.

This thesis is an exloration and exposition of a highly efficient shallow neural network algorithm called word2vec, which was developed by T. Mikolov et al. in order to create vector representations of a language vocabulary such that information about the meaning and usage of the vocabulary words is encoded in the vector space structure. Chapter 1 introduces natural language processing, vector representations of language vocabularies, and the word2vec algorithm. Chapter 2 reviews the basic mathematical theory of deterministic convex optimization. Chapter 3 provides background on some concepts from computer science that are used in the word2vec algorithm: Huffman trees, neural networks, and binary cross-entropy. Chapter 4 provides a detailed discussion of the word2vec algorithm itself and includes a discussion of continuous bag of words, skip-gram, hierarchical softmax, and negative sampling. Finally, Chapter 5 explores some applications of vector representations: word categorization, analogy completion, and language translation assistance.

This thesis describes Burrow-Wheeler compression algorithm. It focuses on each part of Burrow-Wheeler algorithm, most of all on and entropic coders. In section are described methods like move to front, inverse frequences, interval coding, etc. Among the described entropy coders are Huffman, arithmetic and Rice-Golomg coders. In conclusion there is testing of described methods of global structure transformation and entropic coders. Best combinations are compared with the most common compress algorithm.

Signal compression is daily-used tool for memory capacities reduction and for fast data communication. Methods based on wavelet transform seem to be very effective nowadays. Signal decomposition with a suitable bank filters following with coefficients quantization represents one of the available technique. After packing quantized coefficients into one sequence, run length coding together with Huffman coding are implemented. This thesis focuses on compression effectiveness for the different wavelet transform and quantization settings.

This thesis deals with lossless and losing methods of data compressions and their possible applications in the measurement engineering. In the first part of the thesis there is a theoretical elaboration which informs the reader about the basic terminology, the reasons of data compression, the usage of data compression in standard practice and the division of compression algorithms. The practical part of thesis deals with the realization of the compress algorithms in Matlab and LabWindows/CVI.