Cognitive computation of compressed sensing for watermark signal measurement

As an important tool for protecting multimedia contents, scrambling and randomizing of original messages is used in generating digital watermark for satisfying security requirements. Based on the neural perception of high-dimensional data, compressed sensing (CS) is proposed as a new technique in watermarking for improved security and reduced computational complexity. In our proposed methodology, watermark signal is extracted from the CS of the Hadamard measurement matrix. Through construction of the scrambled block Hadamard matrix utilizing a cryptographic key, encrypting the watermark signal in CS domain is achieved without any additional computation required. The extensive experiments have shown that the neural inspired CS mechanism can generate watermark signal of higher security, yet it still maintains a better trade-off between transparency and robustness

Hierarchical and multi-featured fusion for effective gait recognition under variable scenarios

Human identification by gait analysis has attracted a great deal of interest in the computer vision and forensics communities as an unobtrusive technique that is capable of recognizing humans at range. In recent years, significant progress has been made, and a number of approaches capable of this task have been proposed and developed. Among them, approaches based on single source features are the most popular. However the recognition rate of these methods is often unsatisfactory due to the lack of information contained in single feature sources. Consequently, in this paper, a hierarchal and multi-featured fusion approach is proposed for effective gait recognition. In practice, using more features for fusion does not necessarily mean a better recognition rate and features should in fact be carefully selected such that they are complementary to each other. Here, complementary features are extracted in three groups: Dynamic Region Area; Extension and Space features; and 2D Stick Figure Model features. To balance the proportion of features used in fusion a hierarchical feature-level fusion method is proposed. Comprehensive results of applying the proposed techniques to three well-known datasets have demonstrated that our fusion based approach can improve the overall recognition rate when compared to a benchmark algorithm

Automatic extraction of water inundation areas using Sentinel-1 dnata for large plain areas.

Accurately quantifying water inundation dynamics in terms of both spatial distributions and temporal variability is essential for water resources management. Currently, the water map is usually derived from synthetic aperture radar (SAR) data with the support of auxiliary datasets, using thresholding methods and followed by morphological operations to further refine the results. However, auxiliary datasets may lose efficacy on large plain areas, whilst the parameters of morphological operations are hard to be decided in different situations. Here, a heuristic and automatic water extraction (HAWE) method is proposed to extract the water map from Sentinel-1 SAR data. In the HAWE, we integrate tile-based thresholding and the active contour model, in which the former provides a convincing initial water map used as a heuristic input, and the latter refines the initial map by using image gradient information. The proposed approach was tested on the Dongting Lake plain (China) by comparing the extracted water map with the reference data derived from the Sentinel-2 dataset. For the two selected test sites, the overall accuracy of water classification is between 94.90% and 97.21% whilst the Kappa coefficient is within the range of 0.89 and 0.94. For the entire study area, the overall accuracy is between 94.32% and 96.7% and the Kappa coefficient ranges from 0.80 to 0.90. The results show that the proposed method is capable of extracting water inundations with satisfying accuracy

Employing NIR-SWIR hyperspectral imaging to predict the smokiness of Scotch whisky

Scotch Whisky makes a significant contribution to the UK's food and drinks export. The flavour of this high quality spirit is derived naturally from the whisky making process, with smoky aromas being a key character of certain Scotch whiskies. The level of smokiness is determined by the amount of phenolic compounds in the spirit. Phenols are introduced by exposing the barley malt to peat smoke during the kilning process. The current techniques to determine the levels of phenols, such as High Performance Liquid Chromatography (HPLC), are time consuming as they require distillation of the malt prior to analysis. To speed up this process and enable real-time detection before processing, the possibilities of Near-infrared to Short-wave-infrared (NIR-SWIR) Hyperspectral Imaging (HSI) to detect these phenols directly on malted barley are explored. It can be shown that via regression analysis, various levels of phenol concentration used as working levels for whisky production could be estimated to a satisfying degree. To further optimise industrial application, a hyperspectral band selection algorithm is applied that yields good results and reduces computational cost and may open possibilities to employ multispectral rather than hyperspectral cameras in future applications

THE BATTLE FOR SINGLES’ DAY: HOW SOCIAL MEDIA MARKETING CAMPAIGNS BOOST SALES

Numerous studies have shown that social media marketing strategies have positive impacts on the long-term financial performance of firms. However, whether short-term marketing campaigns have any influence on firm revenue remains unknown. This paper examines data from Singles’ Day, the world’s largest shopping event, revealing that firms’ social media efforts have a positive impact on product sales. Furthermore, we find that the two social media effort measures generally thought to have positive impacts on a firm’s long-term financial performance, richness and intensity, have no significant influence on the success of a firm’s short-term marketing campaign. Instead, relevance shows significant and positive impacts. Moreover, we compare the effects of social media marketing yields from company-owned accounts with those of employee-owned accounts, finding that employee-owned accounts have better marketing effects than company-owned ones

EEG-based brain-computer interfaces using motor-imagery: techniques and challenges.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs

Novel gumbel-softmax trick enabled concrete autoencoder with entropy constraints for unsupervised hyperspectral band selection.

As an important topic in hyperspectral image (HSI) analysis, band selection has attracted increasing attention in the last two decades for dimensionality reduction in HSI. With the great success of deep learning (DL)-based models recently, a robust unsupervised band selection (UBS) neural network is highly desired, particularly due to the lack of sufficient ground truth information to train the DL networks. Existing DL models for band selection either depend on the class label information or have unstable results via ranking the learned weights. To tackle these challenging issues, in this article, we propose a Gumbel-Softmax (GS) trick enabled concrete autoencoder-based UBS framework (CAE-UBS) for HSI, in which the learning process is featured by the introduced concrete random variables and the reconstruction loss. By searching from the generated potential band selection candidates from the concrete encoder, the optimal band subset can be selected based on an information entropy (IE) criterion. The idea of the CAE-UBS is quite straightforward, which does not rely on any complicated strategies or metrics. The robust performance on four publicly available datasets has validated the superiority of our CAE-UBS framework in the classification of the HSIs

Target of Rapamycin Regulates Photosynthesis and Cell Growth in Auxenochlorella pyrenoidosa

Auxenochlorella pyrenoidosa is an efficient photosynthetic microalga with autotrophic growth and reproduction, which has the advantages of rich nutrition and high protein content. Target of rapamycin (TOR) is a conserved protein kinase in eukaryotes both structurally and functionally, but little is known about the TOR signalling in Auxenochlorella pyrenoidosa. Here, we found a conserved ApTOR protein in Auxenochlorella pyrenoidosa, and the key components of TOR complex 1 (TORC1) were present, while the components RICTOR and SIN1 of the TORC2 were absent in Auxenochlorella pyrenoidosa. Drug sensitivity experiments showed that AZD8055 could effectively inhibit the growth of Auxenochlorella pyrenoidosa, whereas rapamycin, Torin1 and KU0063794 had no obvious effect on the growth of Auxenochlorella pyrenoidosa a. Transcriptome data results indicated that Auxenochlorella pyrenoidosa TOR (ApTOR) regulates various intracellular metabolism and signaling pathways in Auxenochlorella pyrenoidosa. Most genes related to chloroplast development and photosynthesis were significantly down-regulated under ApTOR inhibition by AZD8055. In addition, ApTOR was involved in regulating protein synthesis and catabolism by multiple metabolic pathways in Auxenochlorella pyrenoidosa. Importantly, the inhibition of ApTOR by AZD8055 disrupted the normal carbon and nitrogen metabolism, protein and fatty acid metabolism, and TCA cycle of Auxenochlorella pyrenoidosa cells, thus inhibiting the growth of Auxenochlorella pyrenoidosa. These RNA-seq results indicated that ApTOR plays important roles in photosynthesis, intracellular metabolism and cell growth, and provided some insights into the function of ApTOR in Auxenochlorella pyrenoidosa

一种基于模糊成像机理的QR码图像快速盲复原方法.

A fast blind restoration method of QR code images was proposed based on a blurred imaging mechanism. On the basis of the research on the centroid invariance of the blurred imaging diffuse light spots, the circular finder pattern is designed. When the image is blurred, the centroid of the pattern and the position of the QR code symbol can be quickly detected by methods such as connected components. Moreover, combined with step edge characteristics, gradient and intensity characteristics, edge detection technology, and optical imaging mechanism, the defocus radius of the blurred QR code image can be quickly and accurately estimated. Furthermore, the Wiener filter is applied to restore the QR code image quickly and effectively. Compared with the other algorithms, the proposed method has improved deblurring results in both structural similarity and peak signal-to-noise ratio, especially in the recovery speed. The average recovery time is 0.329 2 s. Experimental results show that this method can estimate the defocus radius with high accuracy and can quickly realize the blind restoration of QR code images. It has the advantages of rapidity and robustness, which are convenient for embedded hardware implementation and suitable for barcode identification-related industrial Internet of Things application scenarios

A new cost function for spatial image steganography based on 2D-SSA and WMF.

As an essential tool for secure communications, adaptive steganography aims to communicate secret information with the least security cost. Inspired by the Ranking Priority Profile (RPP), we propose a novel two-step cost function for adaptive steganography in this paper. The RPP mainly includes three rules, i.e. Complexity-First rule, the Clustering rule and the Spreading rule, to design a cost function. We use the two-dimensional Singular Spectrum Analysis (2D-SSA) and Weighted Median Filter (WMF) in designing the two-step cost function. The 2D-SSA is employed in selecting the key components and clustering the embedding positions, which follows the Complexity-First rule and the Clustering rule. Also, we deploy the Spreading rule to smooth the resulting image produced by 2D-SSA with WMF. Extensive experiments have shown the efficacy of the proposed method, which has improved performance over four benchmarking approaches against non-shared selection channel attack. It also provides comparable performance in selection-channel-aware scenarios, where the best results are observed when the relative payload is 0.3 bpp or larger. Besides, the proposed approach is much faster than other model-based methods