907 resultados para compression ratio


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PURPOSE: Computer-based feedback systems for assessing the quality of cardiopulmonary resuscitation (CPR) are widely used these days. Recordings usually involve compression and ventilation dependent variables. Thorax compression depth, sufficient decompression and correct hand position are displayed but interpreted independently of one another. We aimed to generate a parameter, which represents all the combined relevant parameters of compression to provide a rapid assessment of the quality of chest compression-the effective compression ratio (ECR). METHODS: The following parameters were used to determine the ECR: compression depth, correct hand position, correct decompression and the proportion of time used for chest compressions compared to the total time spent on CPR. Based on the ERC guidelines, we calculated that guideline compliant CPR (30:2) has a minimum ECR of 0.79. To calculate the ECR, we expanded the previously described software solution. In order to demonstrate the usefulness of the new ECR-parameter, we first performed a PubMed search for studies that included correct compression and no-flow time, after which we calculated the new parameter, the ECR. RESULTS: The PubMed search revealed 9 trials. Calculated ECR values ranged between 0.03 (for basic life support [BLS] study, two helpers, no feedback) and 0.67 (BLS with feedback from the 6th minute). CONCLUSION: ECR enables rapid, meaningful assessment of CPR and simplifies the comparability of studies as well as the individual performance of trainees. The structure of the software solution allows it to be easily adapted to any manikin, CPR feedback devices and different resuscitation guidelines (e.g. ILCOR, ERC).

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Ever since the invention of the internal combustion engine, generating more power and achieving better efficiency has been a major goal for the designers. Variable compression ratio technology is way to achieve those goals. This paper will discuss the method of varying the compression ratio of an inline 4-cylinder engine through the use of a 4-bar linkage and gear mechanism. This mechanism was proven to easily vary the compression ratio of the engine and shows promise of becoming a technology used for future engine designer.

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If the Internet could be used as a method of transmitting ultrasound images taken in the field quickly and effectively, it would bring tertiary consultation to even extremely remote centres. The aim of the study was to evaluate the maximum degree of compression of fetal ultrasound video-recordings that would not compromise signal quality. A digital fetal ultrasound videorecording of 90 s was produced, resulting in a file size of 512 MByte. The file was compressed to 2, 5 and 10 MByte. The recordings were viewed by a panel of four experienced observers who were blinded to the compression ratio used. Using a simple seven-point scoring system, the observers rated the quality of the clip on 17 items. The maximum compression ratio that was considered clinically acceptable was found to be 1:50-1:100. This produced final file sizes of 5-10 MByte, corresponding to a screen size of 320 x 240 pixels, running at 15 frames/s. This study expands the possibilities for providing tertiary perinatal services to the wider community.

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Lossless compression algorithms of the Lempel-Ziv (LZ) family are widely used nowadays. Regarding time and memory requirements, LZ encoding is much more demanding than decoding. In order to speed up the encoding process, efficient data structures, like suffix trees, have been used. In this paper, we explore the use of suffix arrays to hold the dictionary of the LZ encoder, and propose an algorithm to search over it. We show that the resulting encoder attains roughly the same compression ratios as those based on suffix trees. However, the amount of memory required by the suffix array is fixed, and much lower than the variable amount of memory used by encoders based on suffix trees (which depends on the text to encode). We conclude that suffix arrays, when compared to suffix trees in terms of the trade-off among time, memory, and compression ratio, may be preferable in scenarios (e.g., embedded systems) where memory is at a premium and high speed is not critical.

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Coarse Grained Reconfigurable Architectures (CGRAs) are emerging as enabling platforms to meet the high performance demanded by modern applications (e.g. 4G, CDMA, etc.). Recently proposed CGRAs offer time-multiplexing and dynamic applications parallelism to enhance device utilization and reduce energy consumption at the cost of additional memory (up to 50% area of the overall platform). To reduce the memory overheads, novel CGRAs employ either statistical compression, intermediate compact representation, or multicasting. Each compaction technique has different properties (i.e. compression ratio, decompression time and decompression energy) and is best suited for a particular class of applications. However, existing research only deals with these methods separately. Moreover, they only analyze the compaction ratio and do not evaluate the associated energy overheads. To tackle these issues, we propose a polymorphic compression architecture that interleaves these techniques in a unique platform. The proposed architecture allows each application to take advantage of a separate compression/decompression hierarchy (consisting of various types and implementations of hardware/software decoders) tailored to its needs. Simulation results, using different applications (FFT, Matrix multiplication, and WLAN), reveal that the choice of compression hierarchy has a significant impact on compression ratio (up to 52%), decompression energy (up to 4 orders of magnitude), and configuration time (from 33 n to 1.5 s) for the tested applications. Synthesis results reveal that introducing adaptivity incurs negligible additional overheads (1%) compared to the overall platform area.

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This thesis deals with distance transforms which are a fundamental issue in image processing and computer vision. In this thesis, two new distance transforms for gray level images are presented. As a new application for distance transforms, they are applied to gray level image compression. The new distance transforms are both new extensions of the well known distance transform algorithm developed by Rosenfeld, Pfaltz and Lay. With some modification their algorithm which calculates a distance transform on binary images with a chosen kernel has been made to calculate a chessboard like distance transform with integer numbers (DTOCS) and a real value distance transform (EDTOCS) on gray level images. Both distance transforms, the DTOCS and EDTOCS, require only two passes over the graylevel image and are extremely simple to implement. Only two image buffers are needed: The original gray level image and the binary image which defines the region(s) of calculation. No other image buffers are needed even if more than one iteration round is performed. For large neighborhoods and complicated images the two pass distance algorithm has to be applied to the image more than once, typically 3 10 times. Different types of kernels can be adopted. It is important to notice that no other existing transform calculates the same kind of distance map as the DTOCS. All the other gray weighted distance function, GRAYMAT etc. algorithms find the minimum path joining two points by the smallest sum of gray levels or weighting the distance values directly by the gray levels in some manner. The DTOCS does not weight them that way. The DTOCS gives a weighted version of the chessboard distance map. The weights are not constant, but gray value differences of the original image. The difference between the DTOCS map and other distance transforms for gray level images is shown. The difference between the DTOCS and EDTOCS is that the EDTOCS calculates these gray level differences in a different way. It propagates local Euclidean distances inside a kernel. Analytical derivations of some results concerning the DTOCS and the EDTOCS are presented. Commonly distance transforms are used for feature extraction in pattern recognition and learning. Their use in image compression is very rare. This thesis introduces a new application area for distance transforms. Three new image compression algorithms based on the DTOCS and one based on the EDTOCS are presented. Control points, i.e. points that are considered fundamental for the reconstruction of the image, are selected from the gray level image using the DTOCS and the EDTOCS. The first group of methods select the maximas of the distance image to new control points and the second group of methods compare the DTOCS distance to binary image chessboard distance. The effect of applying threshold masks of different sizes along the threshold boundaries is studied. The time complexity of the compression algorithms is analyzed both analytically and experimentally. It is shown that the time complexity of the algorithms is independent of the number of control points, i.e. the compression ratio. Also a new morphological image decompression scheme is presented, the 8 kernels' method. Several decompressed images are presented. The best results are obtained using the Delaunay triangulation. The obtained image quality equals that of the DCT images with a 4 x 4

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This work proposes a parallel genetic algorithm for compressing scanned document images. A fitness function is designed with Hausdorff distance which determines the terminating condition. The algorithm helps to locate the text lines. A greater compression ratio has achieved with lesser distortion

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In this paper, an improved technique for evolving wavelet coefficients refined for compression and reconstruction of fingerprint images is presented. The FBI fingerprint compression standard [1, 2] uses the cdf 9/7 wavelet filter coefficients. Lifting scheme is an efficient way to represent classical wavelets with fewer filter coefficients [3, 4]. Here Genetic algorithm (GA) is used to evolve better lifting filter coefficients for cdf 9/7 wavelet to compress and reconstruct fingerprint images with better quality. Since the lifting filter coefficients are few in numbers compared to the corresponding classical wavelet filter coefficients, they are evolved at a faster rate using GA. A better reconstructed image quality in terms of Peak-Signal-to-Noise-Ratio (PSNR) is achieved with the best lifting filter coefficients evolved for a compression ratio 16:1. These evolved coefficients perform well for other compression ratios also.

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This paper explains the Genetic Algorithm (GA) evolution of optimized wavelet that surpass the cdf9/7 wavelet for fingerprint compression and reconstruction. Optimized wavelets have already been evolved in previous works in the literature, but they are highly computationally complex and time consuming. Therefore, in this work, a simple approach is made to reduce the computational complexity of the evolution algorithm. A training image set comprised of three 32x32 size cropped images performed much better than the reported coefficients in literature. An average improvement of 1.0059 dB in PSNR above the classical cdf9/7 wavelet over the 80 fingerprint images was achieved. In addition, the computational speed was increased by 90.18 %. The evolved coefficients for compression ratio (CR) 16:1 yielded better average PSNR for other CRs also. Improvement in average PSNR was experienced for degraded and noisy images as well

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Data gathering, either for event recognition or for monitoring applications is the primary intention for sensor network deployments. In many cases, data is acquired periodically and autonomously, and simply logged onto secondary storage (e.g. flash memory) either for delayed offline analysis or for on demand burst transfer. Moreover, operational data such as connectivity information, node and network state is typically kept as well. Naturally, measurement and/or connectivity logging comes at a cost. Space for doing so is limited. Finding a good representative model for the data and providing clever coding of information, thus data compression, may be a means to use the available space to its best. In this paper, we explore the design space for data compression for wireless sensor and mesh networks by profiling common, publicly available algorithms. Several goals such as a low overhead in terms of utilized memory and compression time as well as a decent compression ratio have to be well balanced in order to find a simple, yet effective compression scheme.

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Bone Anchored Hearing Implants (BAHI) are routinely used in patients with conductive or mixed hearing loss, e.g. if conventional air conduction hearing aids cannot be used. New sound processors and new fitting software now allow the adjustment of parameters such as loudness compression ratios or maximum power output separately. Today it is unclear, how the choice of these parameters influences aided speech understanding in BAHI users. In this prospective experimental study, the effect of varying the compression ratio and lowering the maximum power output in a BAHI were investigated. Twelve experienced adult subjects with a mixed hearing loss participated in this study. Four different compression ratios (1.0; 1.3; 1.6; 2.0) were tested along with two different maximum power output settings, resulting in a total of eight different programs. Each participant tested each program during two weeks. A blinded Latin square design was used to minimize bias. For each of the eight programs, speech understanding in quiet and in noise was assessed. For speech in quiet, the Freiburg number test and the Freiburg monosyllabic word test at 50, 65, and 80 dB SPL were used. For speech in noise, the Oldenburg sentence test was administered. Speech understanding in quiet and in noise was improved significantly in the aided condition in any program, when compared to the unaided condition. However, no significant differences were found between any of the eight programs. In contrast, on a subjective level there was a significant preference for medium compression ratios of 1.3 to 1.6 and higher maximum power output.

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Asynchronous level crossing sampling analog-to-digital converters (ADCs) are known to be more energy efficient and produce fewer samples than their equidistantly sampling counterparts. However, as the required threshold voltage is lowered, the number of samples and, in turn, the data rate and the energy consumed by the overall system increases. In this paper, we present a cubic Hermitian vector-based technique for online compression of asynchronously sampled electrocardiogram signals. The proposed method is computationally efficient data compression. The algorithm has complexity O(n), thus well suited for asynchronous ADCs. Our algorithm requires no data buffering, maintaining the energy advantage of asynchronous ADCs. The proposed method of compression has a compression ratio of up to 90% with achievable percentage root-mean-square difference ratios as a low as 0.97. The algorithm preserves the superior feature-to-feature timing accuracy of asynchronously sampled signals. These advantages are achieved in a computationally efficient manner since algorithm boundary parameters for the signals are extracted a priori.

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There are a large number of image processing applications that work with different performance requirements and available resources. Recent advances in image compression focus on reducing image size and processing time, but offer no real-time solutions for providing time/quality flexibility of the resulting image, such as using them to transmit the image contents of web pages. In this paper we propose a method for encoding still images based on the JPEG standard that allows the compression/decompression time cost and image quality to be adjusted to the needs of each application and to the bandwidth conditions of the network. The real-time control is based on a collection of adjustable parameters relating both to aspects of implementation and to the hardware with which the algorithm is processed. The proposed encoding system is evaluated in terms of compression ratio, processing delay and quality of the compressed image when compared with the standard method.

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The focus of this thesis is placed on text data compression based on the fundamental coding scheme referred to as the American Standard Code for Information Interchange or ASCII. The research objective is the development of software algorithms that result in significant compression of text data. Past and current compression techniques have been thoroughly reviewed to ensure proper contrast between the compression results of the proposed technique with those of existing ones. The research problem is based on the need to achieve higher compression of text files in order to save valuable memory space and increase the transmission rate of these text files. It was deemed necessary that the compression algorithm to be developed would have to be effective even for small files and be able to contend with uncommon words as they are dynamically included in the dictionary once they are encountered. A critical design aspect of this compression technique is its compatibility to existing compression techniques. In other words, the developed algorithm can be used in conjunction with existing techniques to yield even higher compression ratios. This thesis demonstrates such capabilities and such outcomes, and the research objective of achieving higher compression ratio is attained.

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Medical imaging technologies are experiencing a growth in terms of usage and image resolution, namely in diagnostics systems that require a large set of images, like CT or MRI. Furthermore, legal restrictions impose that these scans must be archived for several years. These facts led to the increase of storage costs in medical image databases and institutions. Thus, a demand for more efficient compression tools, used for archiving and communication, is arising. Currently, the DICOM standard, that makes recommendations for medical communications and imaging compression, recommends lossless encoders such as JPEG, RLE, JPEG-LS and JPEG2000. However, none of these encoders include inter-slice prediction in their algorithms. This dissertation presents the research work on medical image compression, using the MRP encoder. MRP is one of the most efficient lossless image compression algorithm. Several processing techniques are proposed to adapt the input medical images to the encoder characteristics. Two of these techniques, namely changing the alignment of slices for compression and a pixel-wise difference predictor, increased the compression efficiency of MRP, by up to 27.9%. Inter-slice prediction support was also added to MRP, using uni and bi-directional techniques. Also, the pixel-wise difference predictor was added to the algorithm. Overall, the compression efficiency of MRP was improved by 46.1%. Thus, these techniques allow for compression ratio savings of 57.1%, compared to DICOM encoders, and 33.2%, compared to HEVC RExt Random Access. This makes MRP the most efficient of the encoders under study.