Sensor drift is an important problem for biologically inspired Electronic Nose (E-Nose) in industrial cyber-physical systems and their related applications, as it will deteriorate the sensing performance and lower system accuracy. Motivated by the observation that the regular and drift sensing data are oriented to the same high-level decision-making task, but show different low-level data distributions due to domain mismatch caused by sensor drift, this paper seeks to solve the challenging problem by learning a middle-level domain-invariant subspace. To achieve this, a Domain Adaptive Subspace Transfer (DAST) model is developed to transfer the key knowledge of the regular source domain and the drift target domain into an intermediate shared domain for both domain consistency and drift compensation. To be more specific, a transformation matrix is learned to transform the samples from the two domains to the intermediate shared subspace wherein each drift target domain sample can be well reconstructed by a sparse combination of some valuable and informative regular source domain samples, such that knowledge from the two domains is adaptively matched. In addition, the Laplacian manifold regularizations are incorporated to maintain the local affinity manifold of the drift target domain data, and enhance the discriminative structure of the regular source domain data in the shared subspace. The quantitative experiment results on two benchmark gas sensor drift datasets show that the developed DAST model performs well compared to different representative sensor drift compensation methods. 

Domain adaptive subspace transfer model for sensor drift compensation in biologically inspired electronic nose

This paper focuses on the service freshness guarantee for cooperative sensing in the Internet of Everything. Specifically, the peak age of information (AoI) is first introduced to evaluate the information and service freshness. An analytical model is then constructed to decouple the components of peak AoI into the inter-arrival time and transmission time. With the knowledge of identical increments of update arrival and transmission process, a close bound of peak AoI violation probability is derived based on martingale theory. Furthermore, the impact of source node parameter configuration on the peak AoI violation probability are analyzed and a task allocation scheme is proposed to guide cooperative sensing. Numerical analysis validates the tightness of the peak AoI violation probability bound and the effectiveness of the proposed scheme in service freshness guarantee. 

Stochastic Peak Age of Information Guarantee for Cooperative Sensing in Internet of Everything

The soaring data traffic brings tremendous pressure on core networks. Collaborative caching among edge servers and user devices has been regarded as a promising technology for releasing backhaul pressure and reducing content download delay in the next-generation communication network. However, given the capacity constraint of edge servers and caching devices, a challenge is how to exploit these storage resources efficiently. In this paper, we introduce a three-tier collaborative caching architecture for edge-user networks and propose a social-aware graph-based collaborative caching (SGCC) strategy to minimize content download delay. Specifically, the small base station tier and user equipment tier are modeled as a one-tier directed graph. Moreover, we model the physical attributes and social attributes as the weight of the directed edges in the physical link graph and social graph, respectively. Then, we construct a social-aware graph by merging the two-tier topology of the physical link graph and social graph to simplify the caching decision. Further, we design SGCC to cache the most popular content according to the weighted content popularity. Simulation results demonstrate that the proposed SGCC can effectively reduce the average download delay and has a 26.8% lower average download delay, compared with the uniform popular caching baseline.

Social-Aware Graph-Based Collaborative Caching in Edge-User Networks

In this paper, the video delivery strategy is studied under the cooperative framework of cache-enabled fog radio access networks (F-RAN), where multicast beamforming and video-quality selection are both taken into account in terms of different scenarios. In the user-centered scenario, an optimization problem regarding to the network quality of experience (QoE) improvement is first formulated under the essential quality of service (QoS) requirements of the users. Furthermore, an energy efficiency maximization scheme is proposed in the user-dense scenario to improve the resource utilization when network resources may be insufficient to guarantee the QoS requirements of all the users. In particular, a user scheduling scheme is studied in this case. More specifically, the non-convex constraints and fractional object functions in the aforementioned problems are dealt with by successive convex approximation (SCA) technique and the Dinkelbach approach. In order to solve the <inline-formula><tex-math notation="LaTeX">$l_{0}$</tex-math></inline-formula>-norm problem caused by user scheduling, we introduce the <inline-formula><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula>-norm approximation to improve the sparsity of the solution. Finally, the effectiveness of the proposed schemes is validated by extensive simulations.

Resource-Aware Video Delivery in Fog Radio Access Networks: A Joint QoE and QoS Perspective

The emerging physical-layer unclonable attribute-aided authentication (PLUA) schemes are capable of outperforming traditional isolated approaches, with the advantage of having reliable fingerprints. However, conventional PLUA methods face new challenges in artificial intelligence of things (AIoT) applications owing to their limited flexibility. These challenges arise from the distributed nature of AIoT devices and the involved information, as well as the requirement for short end-to-end latency. To address these challenges, we propose a security authentication scheme that utilizes intelligent prediction mechanisms to detect spoofing attack. Our approach is based on a dynamic authentication method using long short term memory (LSTM), where the edge computing node observes and predicts the time-varying channel information of access devices to detect clone nodes. Additionally, we introduce a Savitzky–Golay filter-assisted high order cumulant feature extraction model (SGF-HOCM) for preprocessing channel information. By utilizing future channel attributes instead of relying solely on previous channel information, our proposed approach enables authentication decisions. We have conducted extensive experiments in actual industrial environments to validate our prediction-based security strategy, which has achieved an accuracy of 97%. 

Channel Prediction-Based Security Authentication for Artificial Intelligence of Things

Since the outbreak of the COVID-19, the mainstream media have produced multi-angle and all-round character reports. In order to study the mainstream media’s strategy of character reporting in COVID-19, and to make recommendations for the mainstream media to optimize their strategies. This study, adopting a semiotic perspective, uses content analysis to analyse the content of 186 media reports from People's Daily, Xinhua Daily and China Youth Daily during the 76 days from 23 January 2020 when Wuhan was closed to the public to 8 April 2020 when the city was unsealed. This study derived the basic composition of characters in mainstream media coverage, and also analysed the characteristics and tendencies in mainstream media character coverage based on this, and found that the mainstream media adopted a civilian perspective and a comprehensive character composition in this public health event; it showed similar characteristics in terms of reporting style, and overall showed strong coding characteristics.

Study on characters reports of mainstream media of the COVID-19 from a semiotic perspective

Due to the rapid growth of web conferences, remote screen sharing, and online games, screen content has become an important type of internet media information and over 90% of online media interactions are screen based. Meanwhile, as the main component in the screen content, textual information averagely takes up over 40% of the whole image on various commonly used screen content datasets. However, it is difficult to compress the textual information by using the traditional coding schemes as HEVC, which assumes strong spatial and temporal correlations within the image/video. State-of-the-art screen content coding (SCC) standard as HEVC-SCC still adopts a block-based coding framework and does not consider the text semantics for compression, thus inevitably blurring texts at a lower bitrate. In this paper, we propose a general text semantic-aware screen content coding scheme (TSA-SCC) for ultra low bitrate setting. This method detects the abrupt picture in a screen content video (or image), recognizes textual information (including word, position, font type, font size and font color) in the abrupt picture based on neural networks, and encodes texts with text coding tools. The other pictures as well as the background image after removing texts from the abrupt picture via inpainting, are encoded with HEVC-SCC. Compared with HEVC-SCC, the proposed method TSA-SCC reduces bitrate by up to $3\times $ at a similar compression quality. Moreover, TSA-SCC achieves much better visual quality with less bitrate consumption when encoding the screen content video/image at ultra low bitrates.

TSA-SCC: Text Semantic-Aware Screen Content Coding With Ultra Low Bitrate

QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Haochen Li

Author Tools

Chat about Author

Papers

Study on characters reports of mainstream media of the COVID-19 from a semiotic perspective

TSA-SCC: Text Semantic-Aware Screen Content Coding With Ultra Low Bitrate

QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm