Research on reliable sealing technology of spacecraft rectangular hatch based on boundary control

In view of the large size of the sealing surface of the space large diameter rectangular cabin door, the large number of locking mechanisms and long service life, it is difficult to adjust the multi degree of freedom attitude in the assembly process, the complex assembly and adjustment of the mechanism, the synchronization accuracy is not easy to ensure, the consistency of the multi-point seal compression rate is high, and it is not easy to control. Based on the rotation law of the spatial linkage mechanism, the precise dimension chain control of the hatch is carried out, and the attitude of the multi degree of freedom linkage is adjusted to realize the synchronous compression of multiple compression units of the compression mechanism; Based on the minimum and maximum operating force as the boundary, through the sealing stress simulation, the “boundary control” coupling assembly and adjustment technology is proposed to optimize the sealing structure and realize rapid and efficient assembly. The results show that the vacuum leakage rate is still less than 2.5×10−3 PaL/s after 2000 door opening and closing tests and sealing compression ratio are better than 15%. The long-life test verifies the feasibility and effectiveness of this method, and forms a set of high reliable and long-life sealing assembly and adjustment method of space hatch, which provides technical guidance and reference for the subsequent assembly of similar space hatch mechanisms.

Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

Framework-based Arithmetic Datapath Generation to Explore Parallel Binary Multipliers

Arithmetic modules usually have a significant impact on performance, circuit area, energy, and power in digital circuits of DSP (Digital Signal Processing). Exploring implementation trade-offs in these circuits is of utmost importance in low-power and low-cost devices such as sensors in IoT devices which often have stringent requirements. Multipliers are of particular concern due to their ubiquitous use in DSP algorithms and their inherent implementation complexity. This work proposes a framework to efficiently generalize and explore different compositions of arithmetic operators with an emphasis on parallel binary multipliers, guiding the designer through the micro-architecture development. Several partial product encoders were combined with multiple compression trees to generate multipliers that were synthesized in a commercial 65 nm to obtain area, power, and timing results.

JPEG Artifacts Removal via Compression Quality Ranker-Guided Networks

Existing deep learning-based image de-blocking methods use only pixel-level loss functions to guide network training. The JPEG compression factor, which reflects the degradation degree, has not been fully utilized. However, due to the non-differentiability, the compression factor cannot be directly utilized to train deep networks. To solve this problem, we propose compression quality ranker-guided networks for this specific JPEG artifacts removal. We first design a quality ranker to measure the compression degree, which is highly correlated with the JPEG quality. Based on this differentiable ranker, we then propose one quality-related loss and one feature matching loss to guide de-blocking and perceptual quality optimization. In addition, we utilize dilated convolutions to extract multi-scale features, which enables our single model to handle multiple compression quality factors. Our method can implicitly use the information contained in the compression factors to produce better results. Experiments demonstrate that our model can achieve comparable or even better performance in both quantitative and qualitative measurements.

Image Forgery Detection and Localization using Modified JPEG Ghost

Authenticity of an image taken digitally suffers severe threats as a result of increase in various powerful digital image editing tools. These tools modifies the image contents without leaving footprint of such modifications. We come up with a technique that analyzes digital image forgery detection in JPEG images which goes through multiple compression. Nearly all digital devices uses JPEG as a standard storage format to maintain the storage space. JPEG is a lossy compression standard. By using any image processing tools, when assailant changes any part of a JPEG image and save it, the alter part of the image has different compression artifacts. JPEG ghost algorithm is used to detect disparity in JPEG blocks that rise from improper alignments of JPEG blocks respect to original structure and detect local footprint of JPEG compression. In our work, our proposed technique will modify JPEG ghost detection to detect and localize digital image forgery.

Multiple Impacts and Multiple-Compression Process in the Dynamics of Granular Chains

In this paper, we study the dynamics of one-dimensional chains composed of elastoplastic beads. Three uniform chains, which were experimentally studied in the existing literature, are taken as benchmark examples for manifesting wave propagation induced by multiple impacts between particles and by multiple-compression process in a single contact point. We perform simulations using an elastoplastic contact model developed recently for the binary contact of a sphere. Numerical results show good agreement with the experimental observations, including the profile and amplitude of the incident and reflected solitary waves, the travel time of the wave propagation, and the high-frequency oscillations residing in the high-amplitude stress wave. Our simulations also show that the multiple-compression process of the contact between particles is responsible for the oscillations residing in the pulse profile.

Recurrent Infections in a Patient with Multiple Myeloma

An 80-year-old woman with recurrent urinary tract infection presented to the orthopaedic OPD at KS Hegde Memorial Health Center, Bailur, with gradual onset of generalized weakness, fatigue, anorexia, musculoskeletal pain, and particularly severe backache. The patient was diagnosed to have anemia but renal function tests were normal. X-ray spine showed multiple compression fractures. Serum electrophoresis confirmed the diagnosis of multiple myeloma. In an elderly patient with myalgia, anemia, and recurrent infections, the differential diagnosis of multiple myeloma should be kept in mind as it is often missed by clinicians.