A Single-Stream Pipelined Instruction Decompression System for Embedded Microprocessors

Due to advancements in healthcare monitoring systems, the Internet of Things concepts are proficiently utilized in the medical field to detect and diagnose the physical health problems. The compression of more substantial medical information is a significant issue that requires ample data storage space and takes longer transmission time. Though several compression algorithms are actualized in past cases, there is an absence of an upgraded approach to achieve improved signal compression without influencing signal quality. Hence a proficient signal compression algorithm is proposed in our work to provide an enhanced electrocardiogram (ECG) signal compression without any data loss and to acquire increased compression ratio (CR) and zero construction error. In this proposed approach, the input ECG signal dataset from the MIT‐BIH arrhythmia database gets influenced by noise because of the electrical measuring gadget. Hence, preprocessing is done by the proposed multi‐scoop notch filter (MSNF) to denoise this signal by removing the specified noise frequency range of around (1‐50) Hz. This proposed MSNF is designed with adaptiveness that has achieved the enhanced denoising by adjusting the notch frequency. In addition, to extricate the sophisticated ECG signal features, Fast Fourier Transform is being utilized, and that performs and decomposes the signal elegantly and obtains characteristics of the signal in the frequency domain. After feature extraction, optimal signal compression is performed by our proposed priority‐based convolutional auto‐encoder (PCAE) that provides better compression with almost zero reconstruction error by encoding the signals into lower‐dimensional vectors in convolutional layers which are again reconstructed using a decoding approach. The experimental results are then assessed using the performance metrics that include signal to noise ratio (SNR), CR, and percentage root‐mean‐square difference (PRD). The attained results are 1.83% as average PRD value, average SNR is about 33 dB, and average CR is about 35.2% whereas the traditional CAE approach has average values of 2.05% PRD, 23.45 dB SNR, and 32.2% CR.

Microarray image compression: SLOCO and the effect of information loss

Entropy encoder for low-power low-resources high-quality CFA image compression

Electrocardiogram (ECG) compression can significantly reduce the storage and transmission burden in telemedicine applications. In this paper, an improved ECG data compression method using ASCII character encoding is proposed. A QRS detection algorithm firstly applied to separate QRS and non-QRS region. Then, the QRS regions of ECG signal are compressed using lossless method and non-QRS regions are compressed using lossy method, which improves the compression ratio and minimise the reconstruction error between original and reconstructed ECG signal. All tested ECG records are selected from the PTB database. Statistical results show that the compression performance of the proposed method is better than both lossless and lossy ECG data compression technique. The average compression ratio (CR) and percent root mean square difference (PRD) obtained using proposed ECG data compression method are 27.40 and 4.753 respectively, which is better than other existing ECG data compression method.

To improve the performance of the cone bit bearing seal, experimental study and simulation analysis of seal failure were carried out, and optimization of the seal was conducted. Results show that damages are located on the shoulder of the rubber rings due to high shear stress caused by large compression ratio, and serious wear appears on the sealing face of the metal rings because of unreasonable contact pressure distribution. Optimization was conducted by finite element method on three aspects: the dip angles (3°–8°) of the sealing face, the widths (1.3–3.9 mm) of the sealing face, and the back support structure (10°–15°). These three aspects were optimized one by one. Each aspect was optimized by different values, with the average compression ratio and the contact pressure distribution of the sealing face acting as evaluations. The optimal value of the previous optimization step performs as the static value in the next step, and finally, the optimized structure was selected from the last step. The performance of the optimized seal is significantly improved. The compression ratio of the optimized seal rubber drops to less than 14%. The wear mount of the new structure reduces to 20%–38% of the original structure.

Objective To investigate the clinical prognosis and influencing factors of kidney transplantation from donation after citizens death (DCD) of pediatric donors. Method We retrospectively analyzed the clinical data of kidney transplantation from DCD pediatric donors from October 2012 to October 2015 in the First Affiliated Hospital of Zhengzhou University. We totally obtained 64 kidneys from 32 pediatric donors and performed 58 kidney transplantation containing 52 single kidney transplantations and 6 en-bloc kidney transplantations. Thirty-two donors included 26 single kidney transplantation donors and 6 en-bloc kidney transplantation donors, who were 6.5±4.9 years old (ranging from 28 days to 17 years) on average, with the mean weight of 25.1±14.4 kg (ranging from 2.5 to 67.0 kg) and the mean height of 114.0±33.8 cm (ranging from 50 to 173 cm). Fifty-eight recipients were 22.1±14.3 years old (ranging from 4 years to 52 years) on average, with the mean weight of 40.7±15.8 kg (ranging from 11 to 77 kg) and the mean height of 147.2±21.6 cm (ranging from 84 to 175 cm). Donor/recipient (D/R) body surface area (BSA) ratio ranged from 0.14 to 1.44, and the average ratio was 0.71±0.26. Through retrospective case analysis and regular follow-up, we calculated the incidence of acute rejection, delayed graft function (DGF) and surgical complications of two groups after operation. Meanwhile, we estimated renal function at 1st week, 2nd week, 1st month, 3rd month, 6th month, 12th month and 24th month postoperatively. In addition, human and graft survival rate was also calculated. Result In single kidney transplantation group, 2 recipients underwent acute rejection and resumed to normal after treatment with steroid shock therapy. Three recipients experienced with DGF. Among them, renal function of two recipients resumed to normal after 2 and 3 weeks post-operation respectively, and one pediatric recipient died from severe pulmonary infection. Graft artery stenosis occurred in 2 recipients at 2nd and 3rd month, and these 2 recipients underwent graft artery angioplasty and stent implantation respectively. There was no recurrence of graft artery stenosis during the follow-up at 12th month and 15th month respectively. Four recipients had urinary fistula. After prolongation of indwelling catheter and drainage tube, the urinary fistula was self-healed. One case of ureteral stricture of bladder was given second operation and no recurrence of stricture happened during the follow-up period of 14 months. In en-bloc kidney transplantation group, DGF occurred in 1 recipient and renal function resumed to normal after 3 weeks. Graft thrombosis occurred in 1 recipient and graft nephrectomy was performed 2 weeks post-operation. One recipient experienced with donor abdominal aorta stenosis at 4th month after transplantation, subsequently he received stent implantation and no stenosis recurred during the follow-up period of 12 months. The serum creatinine in 16 recipients during the follow-up period of over 2 years was 132.5±104.6, 117.8±62.8, 101.7±54.0, 84.1±27.9, 69.6±22.6, 74.3±30.8 and 113.8±90.3 μmol/L, and the urea nitrogen was 12.91±8.48, 11.15±5.21, 8.46±3.01, 8.27±2.04, 7.21±1.58, 7.40±1.88 and 11.30±7.38 mmol/L at post-operative 1st week, 2nd week, 1st month, 3rd month, 6th month, 12th month, and 24th month respectively. Conclusion The arterial infusion tube preparation before organ procurement should be noticed, vascular anastomosis details be improved, and meanwhile, recipients who are relatively younger, or have smaller weight and shorter maintenance dialysis time be selected. Kidney transplantation from pediatric DCD donors is feasible and applicable and can achieve relatively favorable preliminary clinical effects. Key words: Kidney transplantation; Donors; Child; Donation

ABSTRACTThe compaction behavior of whole corn kernel (1.8–22.0% mc, w. b.) was investigated by axial compression in an 18.5 mm diameter die using the Amatek Universal Testing Machine at 28°C. The maximum applied pressure in each experiment was 34.6 MN/m2 at loading rates of 0.028, 0.28, and 2.8 mm/s. Sample failure due to oil expression or sample extrusion did not occur in any test run. However, extensive physical rupture of corn kernels occurred at 1.8% moisture content and at lesser but significant levels at 6.6 and 9.7% moisture. At 15.9 and 22.0% moisture, samples were plastically deformed but little macroscopic kernel damage was evident. The effectiveness of compaction increased dramatically as moisture content increased up to 22.0% and then appeared to gradually decline with further increase in moisture. The average compression ratio at zero pressure varied from an average low of 1.70 at 1.8% to an average high of 1.92 at 22.0% mc corresponding to reductions in corn volume of 41 and 47%, respectively. In contrast, loading rate had little effect on density‐pressure profiles. The latter were not adequately characterized by powder compaction models reported in the literature. Substantial volumes of internal tissue gas were estimated with an average maximum of 21% in 15.9% moisture corn kernels.The extent of stress relaxation after termination of compaction increased as loading rate increased and was highest in 15.9% moisture corn. The pressures required for the ejection of compacted corn from the 18.5 mm die varied from 0.5 to 2.1 MN/m2 and revealed an average maximum value at 9.7% moisture. Ejection pressures were substantially independent of loading rate. The most stable compacted corn samples were those produced at the 15.9% moisture level although the compact stability in general was very poor indeed.

Microarray image technology is a powerful tool for monitoring the expression of thousands of genes simultaneously. Each microarray experiment produces immense amounts of image data, and efficient storage and transmission requires compression that utilizes the microarray image's structure and unique analysis goals. We have developed a progressive compression scheme for microarray images which can be either lossy or lossless. Our scheme has a coded data structure that allows fast decoding and reprocessing of image subsets, and includes summary statistics and image segmentation information. Since visual fidelity is not the end goal for microarray images, we introduce a new measure of distortion for lossy compression: the sensitivity of microarray information extraction to compression loss. We find that a lossy compression ratio of 8:1 for cDNA microarrays minimally affects downstream processing. The average lossless compression ratio is 1.83:1 for cDNA images and 2.43:1 for inkjet images, comparable to state-of-the-art lossless schemas, yet with added flexibility and information.

The extensive utilization of the Snappy compression algorithm in digital devices such as smartphones, IoT, and digital cameras has played a crucial role in alleviating demands on network bandwidth and storage space. This paper presents an improved Snappy compression algorithm optimized for implementation on field programmable gate arrays (FPGAs). The proposed algorithm enhances the compression ratio by refining the encoding format of Snappy and introduces an innovative approach utilizing fingerprints within the dictionary to minimize storage space requirements. Additionally, the algorithm incorporates a pipeline structure to optimize performance. Experimental results demonstrate that the proposed algorithm achieves a throughput of 1.6 GB/s for eight hardware kernels. The average compression ratio is 2.27, representing a 6.1% improvement over the state-of-the-art Snappy FPGA implementation. Notably, the proposed algorithm architecture consumes fewer on-chip storage resources compared to other advanced algorithms, striking a balance between logic and storage resource utilization. This optimization leads to higher FPGA resource utilization efficiency. Our design addresses the growing demand for efficient lossless data compression solutions in consumer electronics, ultimately contributing to advancements in modern digital ecosystems.

Aims and Objective:The objective of this study was to evaluate the compression of renal dynamic (RD) study images using singular value decomposition (SVD) technique.Materials and Methods:4600 images of fifty RD study were compressed by using SVD technique. Two Nuclear Medicine (NM) Physicians compared compressed images with their corresponding input images and labeled these as acceptable or unacceptable. The SVD computation time and compression ratio were calculated for each image. The quality of compressed image was also assessed objectively using the following image quality metrics: Error, structural similarity (SSIM), Brightness, global contrast factor, contrast per pixel (CPP), and blur. The error in split function (i.e., the error between split function calculated from compressed image and split function calculated from original image) was computed for every RD study. Wilcoxon signed-rank test with continuity correction was applied to find a statistically significant difference in ROI counts on compressed and original image at.Results:As per NM physicians compressed image frames look identical to the original image frames. Objectively the compressed images were brighter, less noisy, and also have better CPP. Based on the visual assessment, time activity curve generated from original and compressed image frames was identical. There was insignificant difference of ROI counts between the input and compressed image frames of 99m-Tc LLEC RD Study. There was no significant difference between the split renal function estimated from original and its compressed RD study. The average SSIM value, average compression ratio, and SVD computation time were found to be 0.7521, 1.475, and 0.1200.Conclusions:Visually, compressed image was identical to the original image. The percentage compression achieved was found to be up to 58% (compression factor achieved = 1.57). The SVD computation time was approximately 0.12 s for 64 × 64 matrix size image frame.

Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.

In this work, a multi-lead Electrocardiogram (ECG) data compression using principal component analysis (PCA) combined with a machine learning technique is proposed to achieve a high compression ratio (CR) with low reconstruction error (within 2% percentage root mean squared difference, or, PRD). The beat detection procedure was inspired by the Pan-Tompkins algorithm with some necessary modifications. A lead-wise PCA decomposition was performed for dimensionality reduction with a single beat from each lead at a time using a fixed energy reconstruction criteria. The optimal quantization levels of the principal components were allocated using multi-layer perceptron neural network (MLP-NN) using lead clinical features as the input. This MLP-NN was tuned offline by a particle swarm optimization (PSO) generated data for quantization level of coefficients of PC as the reference. The proposed technique was evaluated using 8 types of cardiac abnormalities record from multi-lead ECG data from the PTB Diagnostic ECG database, with an average CR, PRD and PRDN of 16.2, 1.47% and 1.84% respectively. The reconstructed records were clinically acceptable. The proposed technique provides superior performance than few recent published works on multilead ECG compression.

Video encoders and decoders for HEVC-like compression standards require huge external memory bandwidth, which occupies a significant portion of the codec power consumption. To reduce the memory bandwidth, this paper presents a new lossless reference frame recompression algorithm along with a high-throughput hardware architecture. Firstly, hybrid spatial -domain prediction is proposed to combine the merits of DPCM scanning and averaging. The prediction is then enhanced with multiple modes to accommodate various image characteristics. Finally, efficient residual regrouping based on semi-fixed-length (SFL) coding is used to improve the compression performance. Compared to no compression, the proposed scheme can reduce data traffic by an average of 57.6% with no image quality degradation. The average compression ratio is 2.49, an improvement of at least 12.2-13.2%, relative to the state-of-the-art algorithms. By applying a reordered two-step architecture and the two optimizations, residual reuse and simplified coding mode decision, the hardware cost is similar to that of previous reference frame recompression architectures. The computational complexity increase caused by multi-mode prediction affects the HW cost slightly. This work can be implemented with 45.1 k gates for the compressor and 34.5 k gates for the decompressor at 300 MHz, enough to support a 3840 ×2160@60fps video encoder and decoder.

Under the condition of Built-off Self-test (BOST), a novel test data compression scheme based on position information coding is presented, which is targeting test data and test power consumption. The proposed scheme gets the next test stimuli by reversing some bits of the responses corresponding to the previous test vectors. During the process of bit flip, test power problem should be taken into account; both the shift power and capture power during the test should be limited in the scope of security. For shift power, need to modify the scan chain by adding blocking logic; and the capture power reduction through the test cube filling based on the response. Experimental results justify the efficacy of the proposed method in attaining test-data compression; the average compression ratio is 73.63% on the premise of capture power consumption reduced by 20%.

A personal-computer-based electrocardiographic (ECG) noiseless-data compression system has been developed. In addition to using conventional techniques such as a linear predictor and Huffman probability code, the algorithm introduces a new technique to take advantage of the similarity and periodicity of QRS waveforms. The algorithm consists of two cascaded stages. In the first, a linear predictor with three optimal coefficients is used. In the second stage, a portion of the QRS waveform is predicted from previous consecutive QRS complexes to further reduce the bit rate. If no improvement is detected during the second stage, the program automatically employs the first-stage algorithm only. This arrangement ensures the minimum data compression to be equal to that of the conventional technique. A total of nine passages of 15 beats each were processed. Of the total beats, 96% were encoded with both stages of the algorithm and 4% used only the first-stage algorithm. It is concluded that consecutive QRS complexes can predict the subsequent QRS with high accuracy. For noiseless encoding, the average compression ratio was 2.0, and the bit rate was 1015 b/s. >