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The compression method for wellbore trajectory data is crucial for monitoring wellbore stability. However, classical methods like methods based on Huffman coding, compressed sensing, and Differential Pulse Code Modulation (DPCM) suffer from low real-time performance, low compression ratios, and large errors between the reconstructed data and the source data. To address these issues, a new compression method is proposed, leveraging a deep autoencoder for the first time to significantly improve the compression ratio. Additionally, the method reduces error by compressing and transmitting residual data from the feature extraction process using quantization coding and Huffman coding. Furthermore, a mean filter based on the optimal standard deviation threshold is applied to further minimize error. Experimental results show that the proposed method achieves an average compression ratio of 4.05 for inclination and azimuth data; compared to the DPCM method, it is improved by 118.54%. Meanwhile, the average mean square error of the proposed method is 76.88, which is decreased by 82.46% when compared to the DPCM method. Ablation studies confirm the effectiveness of the proposed improvements. These findings highlight the efficacy of the proposed method in enhancing wellbore stability monitoring performance. [ABSTRACT FROM AUTHOR]

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Medical imaging plays a significant role in clinical practice. Storing and transferring a large volume of images can be complex and inefficient. This paper presents the development of a new compression technique that combines the fast discrete curvelet transform (FDCvT) with state table set partitioning in the hierarchical trees (STS) encoding scheme. The curvelet transform is an extension of the wavelet transform algorithm that represents data based on scale and position. Initially, the medical image was decomposed using the FDCvT algorithm. The FDCvT algorithm creates symmetrical values for the detail coefficients, and these coefficients are modified to improve the efficiency of the algorithm. The curvelet coefficients are then encoded using the STS and differential pulse-code modulation (DPCM). The greatest amount of energy is contained in the coarse coefficients, which are encoded using the DPCM method. The finest and modified detail coefficients are encoded using the STS method. A variety of medical modalities, including computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI), are used to verify the performance of the proposed technique. Various quality metrics, including peak signal-to-noise ratio (PSNR), compression ratio (CR), and structural similarity index (SSIM), are used to evaluate the compression results. Additionally, the computation time for the encoding (ET) and decoding (DT) processes is measured. The experimental results showed that the PET image obtained higher values of the PSNR and CR. The CT image provides high quality for the reconstructed image, with an SSIM value of 0.96 and the fastest ET of 0.13 seconds. The MRI image has the shortest DT, which is 0.23 seconds. [ABSTRACT FROM AUTHOR]

Recently, compressing cipher images by using compressive sensing (CS) has attracted a lot of attention. However, the previous CS-based works cannot provide satisfactory compression performance. In this article, a cipher-image compression scheme based on CS and interweaving permutation is proposed to address this concern. Before image compression, the nature image is down-sampled to generate a lot of patches by using interweaving permutation firstly and then each patch is encrypted by a scrambling matrix, where each patch is a down-sampled image. In this way, the encrypted patches are highly correlated from one to each other since they preserve the correlation of the original patches. Therefore, we can encode the cipher image by employing CS and differential pulse-code modulation (DPCM) in an efficient way. To reconstruct the image effectively, an iterative shrinkage-thresholding (IST) algorithm is developed. Compared with the previous CS-based works, our scheme achieves better ratio-distortion (R-D) performance. [ABSTRACT FROM AUTHOR]

Ultra-stable room-sized environmental chambers may involve significant power variations leading to hysteresis and nonlinearities of the stabilization system. This paper describes utilization of a pulse code modulation control which significantly reduces observed nonlinearity of the chamber actuation system. Stability of environmental conditions for temperature Δ T = ± 25 mK (15–35 °C dynamic range), relative humidity Δ R h = ± 0. 1 % (3%–95% dynamic range), and pressure Δ P = ± 12 Pa (± 500 Pa dynamic range) was achieved within 27 m 3 chamber volume, which is two orders of magnitude better than the corresponding stabilization achievable using a standard linear proportional–integral–derivative (PID) controller. Utilizing this chamber we have demonstrated two orders of magnitude improvement of the operational relative frequency stability of ultrastable quartz oscillators (USO), reaching 2 × 1 0 − 13 stability at 1- 1 0 5 s averaging time. • The chamber allow the development of instruments before the environment is available. • The chambers simulate conditions to test the failure rates of the novel instruments. • Control of the environmental factors is the key to qualify the source of failure. • The chamber is tuned to qualify atomic clocks, masers, oscillators, frequency units. • A pulse code modulation control reduces nonlinearity of the chamber actuation system. [ABSTRACT FROM AUTHOR]