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A new approach to wire-frame tracking for semantic model-based moving image coding

SIGNAL PROCESSING-IMAGE COMMUNICATION 2000年第6期15卷 567-580页

作者： Antoszczyszyn, PM Hannah, JM Grant, PM Univ Edinburgh Dept Elect Engn Edinburgh EH9 3JL Midlothian Scotland

Automatic wire-frame fitting and automatic wire-frame tracking are the two most important and most difficult issues associated with semantic-based moving image coding. A novel approach to high-speed tracking of important facial features is presented as a part of a complete fitting-tracking system we have developed. The method allows real-time processing of head-and-shoulders sequences using software tools only. The algorithm is based on eigenvalue decomposition of the sub-images extracted from subsequent frames of the Video sequence. Since each facial feature (the left eye, the right eye, the nose and the lips) is tracked separately, the algorithm can be easily adapted for a parellel machine. The algorithm was tested on numerous widely used head-and-shoulders video sequences containing speaker's head pan, rotation and zoom with remarkably good results. The experiments we have carried out prove that it is possible to maintain tracking even when the facial features are partially occluded. (C) 2000 Elsevier Science B.V. All rights reserved.

关键词： tracking image coding semantic coding model-based coding wire-frame model principal components analysis

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Research on sentiment analysis of hotel review text based on BERT-TCN-BiLSTM-attention model

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ARRAY 2025年 25卷

作者： Chi, Dianwei Huang, Tiantian Jia, Zehao Zhang, Sining Yantai Inst Technol Sch Artificial Intelligence Yantai 264000 Peoples R China Belarusian State Univ Dept Oriental Studies Minsk BELARUS

Due to the high semantic flexibility of Chinese text, the difficulty of word separation, and the problem of multiple meanings of one word, a sentiment analysis model based on the combination of BERT dynamic semantic coding with temporal convolutional neural network (TCN), bi-directional long- and short-term memory network (BiLSTM), and Self-Attention mechanism (Self-Attention) is proposed. The model uses BERT pre-training to generate word vectors as model input, uses the causal convolution and dilation convolution structures of TCN to obtain higher-level sequential features, then passes to the BiLSTM layer to fully extract contextual sentiment features, and finally uses the Self-Attention mechanism to distinguish the importance of sentiment features in sentences, thus improving the accuracy of sentiment classification. The proposed model demonstrates superior performance across multiple datasets, achieving accuracy rates of 89.4 % and 91.2 % on the hotel review datasets C1 and C2, with corresponding F1 scores of 0.898 and 0.904. These results, which surpass those of the comparative models, validate the model's effectiveness across different datasets and highlight its robustness and generalizability in sentiment analysis. It also shows that BERT-based coding can improve the model's performance more than Word2Vec.

关键词： BERT TCN BiLSTM Attention Sentiment analysis NLP semantic coding

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A survey on semantic communications:Technologies,solutions,applications and challenges

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Digital Communications and Networks 2024年第3期10卷 528-545页

作者： Yating Liu Xiaojie Wang Zhaolong Ning MengChu Zhou Lei Guo Behrouz Jedari School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Baotou Vocational&Technical College Baotou 014035China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102USA Nokia Finland

semantic Communication(SC)has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission throughput in wireless networks,beyond the theoretical capacity *** the extensive research on SC,there is a lack of comprehensive survey on technologies,solutions,applications,and challenges for *** this article,the development of SC is first reviewed and its characteristics,architecture,and advantages are ***,key technologies such as semantic extraction,semantic encoding,and semantic segmentation are discussed and their corresponding solutions in terms of efficiency,robustness,adaptability,and reliability are *** of SC to UAV communication,remote image sensing and fusion,intelligent transportation,and healthcare are also presented and their strategies are ***,some challenges and future research directions are presented to provide guidance for further research of SC.

关键词： semantic communication semantic coding semantic extraction semantic communication framework semantic communication applications

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Representing and compressing facial animation parameters using facial action basis functions

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1999年第3期9卷 405-410页

作者： Ahlberg, J Li, HB Linkoping Univ Dept Elect Engn Image Coding Grp S-58183 Linkoping Sweden

In model-based, or semantic, coding, parameters describing the nonrigid motion of objects, e.g., the mimics of a face, are of crucial interest. The facial animation parameters (FAP's) specified in MPEG-4 compose a very rich set of such parameters, allowing a wide range of facial motion. However, the FAP's are typically correlated and also constrained in their motion due to the physiology of the human face. We seek here to utilize this spatial correlation to achieve efficient compression. As it does not introduce: any interframe delay, the method is suitable for interactive applications, e.g., videophone and interactive video, where low delay is a vital issue.

关键词： face animation model-based coding MPEG-4 semantic coding synthetic/natural hybrid coding

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semantic-Aware Bundle Delivery in Space Disruption-Tolerant Networks via Cross-Layer Design on BP and LTP

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第9期23卷 8742-8756页

作者： Gao, Ronghao Li, Yue Zhang, Qinyu Yang, Zhihua Harbin Inst Technol Commun Engn Res Ctr Shenzhen 518055 Guangdong Peoples R China Pengcheng Lab Shenzhen 518055 Guangdong Peoples R China

In large-span space communication, the current bundle delivery mechanism using the Disruption-Tolerant Networks (DTN) technique confronts huge challenges such as high packet loss rate and huge latency. These challenges incur obviously low goodput when delivering scientific and engineering data for the target missions, such as instructions, text, and images. However, the context correlations in these data are not yet excavated to resist the above challenges by current works. To address this issue, therefore, we propose a semantic-aware bundle delivery mechanism for context-dependent data via a cross-layer design on Bundle Protocol (BP) and Licklider Transmission Protocol (LTP), which has the excellent error-tolerance capability by the well-designed semantic-oriented Automatic Repeat reQuest (ARQ) scheme. In particular, the jointed cross-layer design consists of a semantic Blocking (SB) and semantic coding (SC)-based Bundle Updating (BU) mechanism and a dynamic Red/Green-part Allocation method based on semantic Importance (RGA-SI) for bundles and segments in the two layers. Simulation results show that the proposed mechanism can reduce data latency and improve goodput from about 50% to 70% compared with the current bundle delivery mechanism in DTN with optimal segment size, especially under bad channel conditions.

关键词： semantics Encoding Space missions Packet loss Cross layer design Reliability Protocols Automatic repeat reQuest (ARQ) bundle licklider transmission protocol (LTP) semantic coding

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Wireless End-to-End Image Transmission System Using semantic Communications

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IEEE ACCESS 2023年 11卷 37149-37163页

作者： Lokumarambage, Maheshi U. Gowrisetty, Vishnu Sai Sankeerth Rezaei, Hossein Sivalingam, Thushan Rajatheva, Nandana Fernando, Anil Univ Strathclyde Dept Comp & Informat Sci Glasgow G1 1XQ Scotland Univ Oulu Ctr Wireless Commun CWC Oulu 90570 Finland

semantic communication is considered the future of mobile communication, which aims to transmit data beyond Shannon's theorem of communications by transmitting the semantic meaning of the data rather than the bit-by-bit reconstruction of the data at the receiver's end. The semantic communication paradigm aims to bridge the gap of limited bandwidth problems in modern high-volume multimedia application content transmission. Integrating AI technologies with the 6G communications networks paved the way to develop semantic communication-based end-to-end communication systems. In this study, we have implemented a semantic communication-based end-to-end image transmission system, and we discuss potential design considerations in developing semantic communication systems in conjunction with physical channel characteristics. A Pre-trained GAN network is used at the receiver as the transmission task to reconstruct the realistic image based on the semantic segmented image at the receiver input. The semantic segmentation task at the transmitter (encoder) and the GAN network at the receiver (decoder) is trained on a common knowledge base, the COCO-Stuff dataset. The research shows that the resource gain in the form of bandwidth saving is immense when transmitting the semantic segmentation map through the physical channel instead of the ground truth image in contrast to conventional communication systems. Furthermore, the research studies the effect of physical channel distortions and quantization noise on semantic communication-based multimedia content transmission.

关键词： semantics Communication systems Receivers Decoding Wireless communication Image communication Bandwidth Generative adversarial networks End-to-end communication generative adversarial network (GAN) polar code semantic coding semantic communication

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semantics-Native Communication via Contextual Reasoning

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第3期9卷 604-617页

作者： Seo, Hyowoon Park, Jihong Bennis, Mehdi Debbah, Merouane Kwangwoon Univ Dept Elect & Commun Engn Seoul 01897 South Korea Deakin Univ Sch Informat Technol Geelong Vic 3220 Australia Univ Oulu Ctr Wireless Commun Oulu 90014 Finland Technol Innovat Inst AI & Digital Sci Res Ctr Abu Dhabi U Arab Emirates Univ Paris Saclay Cent Supelec F-91192 Gif Sur Yvette France

Recently, machine learning (ML) has shown its effectiveness in improving communication efficiency by reinstating the semantics of bits. To understand its underlying principles, we propose a novel stochastic model of semantic communication, dubbed semantics-native communication (SNC). Inspired from human communication, we consider a point-to-point SNC scenario where a speaker has an intention of referring to an entity, extracts its semantic concepts, and maps these concepts into symbols that are communicated to a target listener through the traditional Shannon communication channel. Next, we recall rational humans who can communicate more efficiently by reasoning about the others' contexts before communication, referred to as contextual reasoning. This motivates us to propose a novel SNC framework harnessing agent states as side information in a way that the speaker locally and iteratively communicates with a virtual agent having the listener's state, and vice versa. Theoretically, we prove the convergence of contextual reasoning, at which it minimizes the bit length while guaranteeing a target reliability. Simulation results corroborate that contextual reasoning based SNC can significantly reduce bit lengths, and be a robust solution to imperfect agent states by quantizing the entity-concept-symbol mapping before contextual reasoning.

关键词： semantics Cognition Channel coding Encoding Decoding Stochastic processes Data models semantic communication semantic coding contextual reasoning semantics-native

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Deep Learning-Enabled semantic Communication Systems With Task-Unaware Transmitter and Dynamic Data

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2023年第1期41卷 170-185页

作者： Zhang, Hongwei Shao, Shuo Tao, Meixia Bi, Xiaoyan Letaief, Khaled B. Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Huawei Ottawa Res Ctr Ottawa ON K2K 3J1 Canada Hong Kong Univ Sci & Technol Elect & Comp Engn Dept Hong Kong Peoples R China

Existing deep learning-enabled semantic communication systems often rely on shared background knowledge between the transmitter and receiver that includes empirical data and their associated semantic information. In practice, the semantic information is defined by the pragmatic task of the receiver and cannot be known to the transmitter. The actual observable data at the transmitter can also have non-identical distribution with the empirical data in the shared background knowledge library. To address these practical issues, this paper proposes a new neural network-based semantic communication system for image transmission, where the task is unaware at the transmitter and the data environment is dynamic. The system consists of two main parts, namely the semantic coding (SC) network and the data adaptation (DA) network. The SC network learns how to extract and transmit the semantic information using a receiver-leading training process. By using the domain adaptation technique from transfer learning, the DA network learns how to convert the data observed into a similar form of the empirical data that the SC network can process without re-training. Numerical experiments show that the proposed method can be adaptive to observable datasets while keeping high performance in terms of both data recovery and task execution.

关键词： Task-unaware semantic communication semantic coding domain adaptation

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Adaptive Bit Rate Control in semantic Communication With Incremental Knowledge-Based HARQ

IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY

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IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY 2022年 3卷 1076-1089页

作者： Zhou, Qingyang Li, Rongpeng Zhao, Zhifeng Xiao, Yong Zhang, Honggang Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Lab Hangzhou 311121 Peoples R China Huazhong Univ Sci & Technol Sch Elect Informat & Commun Wuhan 430074 Peoples R China Pengcheng Natl Lab Guangzhou Base Guangzhou 510555 Peoples R China

semantic communication has witnessed a great progress with the development of natural language processing (NLP) and deep learning (DL). Although existing semantic communication technologies can effectively reduce errors in semantic interpretation, most of these solutions adopt a fixed bit length structure, along with a rigid transmission scheme, which is inefficient and lacks scalability faced with different meanings and signal-to-noise ratio (SNR) conditions. In this paper, we explore the impact of adaptive bit lengths on semantic coding (SC) under various channel conditions. First, we propose progressive semantic hybrid automatic repeat request (HARQ) schemes that utilize incremental knowledge (IK) to simultaneously reduce the communication cost and semantic error. On top of this, we design a novel semantic encoding solution with multi-bit length selection. In this fashion, the transmitter employs a policy network to decide the appropriate coding rate, so as to secure the correct information delivery at the cost of minimal bits. Moreover, a specific denoiser is further introduced to reduce the semantic errors encountered in the transmission process according to the semantic characteristics of context. Extensive simulation results have been conducted to verify the effectiveness of the proposed solution.

关键词： semantics Decoding Encoding Bit rate Transformers Noise reduction Channel coding semantic communication semantic coding joint source channel coding deep learning neural network transformer end-to-end communication HARQ

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A novel method of symbolic representation in diving data mining: A case study of highways in China

A novel method of symbolic representation in diving data min...

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International Conference on Modern Computer Science and Applications (MCSA)

作者： Sun, Chuan Liu, Wei Chu, Duanfeng Li, Wushuang Lu, Zhenji Wang, Jianyu Huanggang Normal Univ Coll Transportat Huanggang Peoples R China Shenzhen Univ Coll Informat Engn Shenzhen Peoples R China Wuhan Univ Technol Intelligent Transport Syst Res Ctr Wuhan Hubei Peoples R China Shihezi Univ Coll Informat Sci & Technol Shihezi Peoples R China Delft Univ Technol Fac Mech Maritime & Mat Engn Delft Netherlands

Vehicle field test can be conducted smoothly because of the automobile-mounted monitoring system and abundant diving data have been collected. Driving data mining is in an urgent need of new thoughts introduced to break through the original technical bottleneck. This paper presented a novel method of symbolic representation in diving data mining and applied the idea of time series symbolization to traffic engineering. The sample data is processed by normalization, dimensionality reduction, discretization, and symbolization based on the three steps of symbolic aggregate approximation (SAX) with driving data characteristics taken into adequate consideration. The results showed that the high-dimensionality miscellaneous driving time series data was rationally converted into highly readable, easy to search and locate symbolic series after semantic encoding, and the main characteristics of time series data was preserved after a substantial reduction of data dimensionality. Finally, the paper demonstrated the positive effects of this method on the analysis of actual vehicle driving safety based on case study, and it explored the application of SAX to speed and acceleration data from driving data set.

关键词： diving data semantic coding spatio-temporal symbolic aggregate approximation time series

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