A novel steganalysis method with deep learning for different texture complexity images

Zhong, Shangping; Jia, Cunmin; Chen, Kaizhi; Dai, Peng

doi:10.1007/s11042-018-6573-5

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…This study opted to construct a rice recognition model using the ResNet network, known for its capability to train neural networks with an increased number of layers while avoiding issues such as gradient disappearance or gradient explosion. For image classification using ResNet, which mainly involves efficiency as well as accuracy issues, Zhong et al [24] divides and processes images with different complexity and selects the network structure according to the average complexity of the training images. Through experiments, they proved that this method can reduce the labor cost and time cost of the algorithm under the condition that the deep learning algorithm is guaranteed.…”

Section: Discussionmentioning

confidence: 99%

Deep-Learning-Based Rice Phenological Stage Recognition

Qin

Yuan

et al. 2023

Remote Sensing

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Crop phenology is an important attribute of crops, not only reflecting the growth and development of crops, but also affecting crop yield. By observing the phenological stages, agricultural production losses can be reduced and corresponding systems and plans can be formulated according to their changes, having guiding significance for agricultural production activities. Traditionally, crop phenological stages are determined mainly by manual analysis of remote sensing data collected by UAVs, which is time-consuming, labor-intensive, and may lead to data loss. To cope with this problem, this paper proposes a deep-learning-based method for rice phenological stage recognition. Firstly, we use a weather station equipped with RGB cameras to collect image data of the whole life cycle of rice and build a dataset. Secondly, we use object detection technology to clean the dataset and divide it into six subsets. Finally, we use ResNet-50 as the backbone network to extract spatial feature information from image data and achieve accurate recognition of six rice phenological stages, including seedling, tillering, booting jointing, heading flowering, grain filling, and maturity. Compared with the existing solutions, our method guarantees long-term, continuous, and accurate phenology monitoring. The experimental results show that our method can achieve an accuracy of around 87.33%, providing a new research direction for crop phenological stage recognition.

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Section: Discussionmentioning

confidence: 99%

Deep-Learning-Based Rice Phenological Stage Recognition

Qin

Yuan

et al. 2023

Remote Sensing

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“…The content of a product display includes new features, functional testing, customer evaluations and needs, and pre-purchase trials, which can all serve to attract consumers' interest, especially when they can actually touch and feel products (MBA Skool, 2019). In terms of advantages and disadvantages, consumers may have difficulty using product displays, and product samples may become worn after being on display for a long time (Zhong et al. , 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the effectiveness of VR-based product demonstrations featuring items of furniture

Cheng-yao

2022

LHT

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PurposeThis paper aims to present a case study of virtual-reality-based product demonstrations featuring items of furniture. The results will be of use in further design and development of virtual-reality-based product demonstration systems and could also support effective student learning.Design/methodology/approachA new method was introduced to guide the experiment by confirming orthogonal arrays. User interactions were then planned, and a furniture demonstration system was implemented. The experiment comprised two stages. In the evaluation stage, participants were invited to experience the virtual-reality (VR)-based furniture demonstration system and complete a user experience (UX) survey. Taguchi-style robust design methods were used to design orthogonal table experiments and planning and design operation methods were used to implement an experimental display system in order to obtain optimized combinations of control factors and levels. The second stage involved a confirmatory test for the optimized combinations. A pilot questionnaire was first applied to survey demonstration scenarios that are important to customers.FindingsThe author found in terms of furniture products, product interactive display through VR can achieve good user satisfaction through quality design planning. VR can better grasp the characteristics of products than paper catalogs and website catalogs. And VR can better grasp the characteristics of products than online videos. For “interactive inspection”, “function simulation”, “style customization” and “set-out customization” were the most valuable demonstration scenarios for customers. The results of the experiment confirmed that the “overall rating”, “hedonic appeal” and “practical quality” were the three most important optimized operating methods, constituting a benchmark of user satisfaction.Originality/valueThe author found that it is possible to design and build a VR-based furniture demonstration system with a good level of usability when a suitable quality design method is applied. The optimized user interaction indicators and implementation experience for the VR-based product demonstration presented in this study will be of use in further design and development of similar systems.

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“…Compared to the conventional schemes, DL has been shown to extract powerfully the inherent characteristic of signals [20] and thus has been qualified when overcoming multiple problems in wireless communications field [21][22][23][24][25]. FCNN was utilized into channel estimation and pilot design [11,12].…”

Section: Related Workmentioning

confidence: 99%

A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications

Zhu

Sheng

Fang

et al. 2020

J Wireless Com Network

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In vehicular communications using IEEE 802.11p, estimating channel frequency response (CFR) is a remarkably challenging task. The challenge for channel estimation (CE) lies in tracking variations of CFR due to the extremely fast time-varying characteristic of channel and low density pilot. To tackle such problem, inspired by image super-resolution (ISR) techniques, a deep learning-based temporal spectral channel network (TS-ChannelNet) is proposed. Following the process of ISR, an average decision-directed estimation with time truncation (ADD-TT) is first presented to extend pilot values into tentative CFR, thus tracking coarsely variations. Then, to make tentative CFR values accurate, a super resolution convolutional long short-term memory (SR-ConvLSTM) is utilized to track channel extreme variations by extracting sufficiently temporal spectral correlation of data symbols. Three representative vehicular environments are investigated to demonstrate the performance of our proposed TS-ChannelNet in terms of normalized mean square error (NMSE) and bit error rate (BER). The proposed method has an evident performance gain over existing methods, reaching about 84.5% improvements at some high signal-noise-ratio (SNR) regions.

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A novel steganalysis method with deep learning for different texture complexity images

Cited by 8 publications

References 25 publications

Deep-Learning-Based Rice Phenological Stage Recognition

Deep-Learning-Based Rice Phenological Stage Recognition

Exploring the effectiveness of VR-based product demonstrations featuring items of furniture

A deep learning-aided temporal spectral ChannelNet for IEEE 802.11p-based channel estimation in vehicular communications

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