Bit-Error Aware Lossless Image Compression with 2D-Layer-Block Coding

With IoT development, it becomes more popular that image data is transmitted via wireless communication systems. If bit errors occur during transmission, the recovered image will become useless. To solve this problem, a bit-error aware lossless image compression based on bi-level coding is proposed for gray image compression. But bi-level coding has not considered the inherent statistical correlation in 2D context region. To resolve this shortage, a novel variable-size 2D-block extraction and encoding method with built-in bi-level coding for color image is developed to decrease the entropy of information and improve the compression ratio. A lossless color transformation from RGB to the YCrCb color space is used for the decorrelation of color components. Particularly, the layer-extraction method is proposed to keep the Laplacian distribution of the data in 2D blocks which is suitable for bi-level coding. In addition, optimization of 2D-block start bits is used to improve the performance. To evaluate the performance of our proposed method, many experiments including the comparison with state-of-the-art methods, the effects with different color space, etc. are conducted. The comparison experiments under a bit-error environment show that the average compression rate of our method is better than bi-level, Jpeg2000, WebP, FLIF, and L3C (deep learning method) with hamming code. Also, our method achieves the same image quality with the bi-level method. Other experiments illustrate the positive effect of built-in bi-level encoding and encoding with zero-mean values, which can maintain high image quality. At last, the results of the decrease of entropy and the procedure of our method are given and discussed.

An Improved Adaptive Subsurface Phytoplankton Layer Detection Method for Ocean Lidar Data

Phytoplankton, as the foundation of primary production, is of great significant for the marine ecosystem. The vertical distribution of phytoplankton contains key information about marine ecology and the optical properties of water bodies related to remote sensing.The common methods to detect subsurface phytoplankton biomass are often in situ measurements and passive remote sensing; however, the bio-argo measurement is discrete and costly, and the passive remote sensing measurement is limited to obtain the vertical information. As a component of active remote sensing, lidar technology has been proved as an effective method for mapping the vertical distribution of phytoplankton. In the past years, there have been few studies on the phytoplankton layer extraction method for lidar data. The existing subsurface layer extraction algorithms are often non-automatic, which need manual intervention or empirical parameters to set the layer extraction threshold. Hence, an improved adaptive subsurface phytoplankton layer detection method was proposed, which incorporates a curve fitting method and a robust estimation method to determine the depth and thickness of subsurface phytoplankton scattering layer. The combination of robust estimation method can realize automatic calculation of layer detection threshold according to the characteristic of each lidar signal, instead of an empirical fixed value used in previous works. In addition, the noise jamming signal can also be effectively detected and removed. Lidar data and in situ spatio-temporal matching Chlorophyll-a profile data obtained in Sanya Bay in 2018 was used for algorithm verification. The example result of step-by-step process illustrates that the improved method is available for adaptive threshold determination for layer detection and redundant noise signals elimination. Correlation analysis and statistical hypothesis testing shows the retrieved subsurface phytoplankton maximum depth by the improved method and in situ measurement is highly relevant. The absolute difference of layer maximum depth between lidar data and in situ data for all stations is less than 0.75 m, and mean absolute difference of layer thickness difference is about 1.74 m. At last, the improved method was also applied to the lidar data obtained near Wuzhizhou Island seawater, which proves that the method is feasiable and robust for various sea areas.

An Edge – IoT Framework and Prototype based on Blockchain for Smart Healthcare Applications

The Internet of Things (IoT) and the integration of medical devices perform hand-to-hand solutions and comfort to their users. With the inclusion of IoT under medical devices a hybrid (IoMT) is formulated. This features integrated computation and processing of data via dedicated servers. The IoMT is supported with an edge server to assure the mobility of data and information. The backdrop of IoT is a networking framework and hence, the security of such devices under IoT and IoMT is at risk. In this article, a framework and prototype for secure healthcare application processing via blockchain are proposed. The proposed technique uses an optimized Crow search algorithm for intrusion detection and tampering of data extraction in IoT environment. The technique is processed under deep convolution neural networks for comparative analysis and coordination of data security elements. The technique has successfully extracted the instruction detection from un-peer source with a source validation of 100 IoT nodes under initial intervals of 25 nodes based on block access time, block creation, and IPFS storage layer extraction. The proposed technique has a recorded performance efficiency of 92.3%, comparable to trivial intrusion detection techniques under Deep Neural Networks (DNN) supported algorithms.

Attention Multi-Scale Network for Automatic Layer Extraction of Ice Radar Topological Sequences

Analyzing the surface and bedrock locations in radar imagery enables the computation of ice sheet thickness, which is important for the study of ice sheets, their volume and how they may contribute to global climate change. However, the traditional handcrafted methods cannot quickly provide quantitative, objective and reliable extraction of information from radargrams. Most traditional handcrafted methods, designed to detect ice-surface and ice-bed layers from ice sheet radargrams, require complex human involvement and are difficult to apply to large datasets, while deep learning methods can obtain better results in a generalized way. In this study, an end-to-end multi-scale attention network (MsANet) is proposed to realize the estimation and reconstruction of layers in sequences of ice sheet radar tomographic images. First, we use an improved 3D convolutional network, C3D-M, whose first full connection layer is replaced by a convolution unit to better maintain the spatial relativity of ice layer features, as the backbone. Then, an adjustable multi-scale module uses different scale filters to learn scale information to enhance the feature extraction capabilities of the network. Finally, an attention module extended to 3D space removes a redundant bottleneck unit to better fuse and refine ice layer features. Radar sequential images collected by the Center of Remote Sensing of Ice Sheets in 2014 are used as training and testing data. Compared with state-of-the-art deep learning methods, the MsANet shows a 10% reduction (2.14 pixels) on the measurement of average mean absolute column-wise error for detecting the ice-surface and ice-bottom layers, runs faster and uses approximately 12 million fewer parameters.

Elimination of diatomite dust in the longwall extraction section of the Sig mine in western Algeria

Research relevance. Dust generated by mining machines due to continuous loading and transport equipment movement especially in mines with no optimal ventilation conditions is a major problem for decision-makers. Dust directly affects mine capacity and exposes workers' lives to a risk of chronic disease due to the inhalation of large quantities of this dust and toxic gases during a seven-hour or eight-hour shift. Dust control methods in longwall mines vary from one mine to another, depending on ore composition. Research aim. In this article we will examine the current flow process of eliminating dust in the longwall working section and propose another process to ensure optimal working conditions in the diatomite mine. Methodology. Analysis of the technical equipment which is currently used in mining and geological operations and evaluation of its application at the facility under study represents a relevant approach to production efficiency improvement. The present research considers the possibility of using the floodedbed scrubber system which was mainly used in underground coal mines to monitor dust in the longwall section. It has been evaluated how efficient the scrubber absorbs the dust generated as a results of the mineralized layer extraction, the concentration of the coal dust in the working section making up from 0.7 to 8.8 mg/m3, while the dust produced during the extraction of the mineralized layer in the underground diatomite deposit is 0.38–1.21 mg/m3 in the working section. Research results. The research resulted in the positive attitude towards the possibility of applying the flooded-bed scrubber, which is used in coal mines, at the longwall extraction sections of the Sig mine. It will significantly reduce the concentration of dust in the longwall section. Conclusion. In order to set the appropriate working environment inside the mine, an effective method of removing dust must be developed, it is especially important to remove dust during work, which will contribute greatly to boosting productivity and reducing diseases among workers.

Fast Single-Image HDR Tone-Mapping by Avoiding Base Layer Extraction

The tone-mapping algorithm compresses the high dynamic range (HDR) information into the standard dynamic range for regular devices. An ideal tone-mapping algorithm reproduces the HDR image without losing any vital information. The usual tone-mapping algorithms mostly deal with detail layer enhancement and gradient-domain manipulation with the help of a smoothing operator. However, these approaches often have to face challenges with over enhancement, halo effects, and over-saturation effects. To address these challenges, we propose a two-step solution to perform a tone-mapping operation using contrast enhancement. Our method improves the performance of the camera response model by utilizing the improved adaptive parameter selection and weight matrix extraction. Experiments show that our method performs reasonably well for overexposed and underexposed HDR images without producing any ringing or halo effects.