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sliding window and interleaver design

ELECTRONICS LETTERS 2001年第21期37卷 1299-1300页

作者： Barbuleseu, SA Univ S Australia Inst Telecommun Res Mawson Lakes SA 5095 Australia

Terminating the trellis was considered essential to lower the bit error probability in a turbo coded system. When a sliding window algorithm is used, this issue becomes even more important. It is shown that trellis termination can be completely ignored by using an interleaver that takes into account the particular window size of the sliding window algorithm.

关键词： turbo coded system interleaver design turbo codes sliding window algorithm interleaved codes Codes trellis codes error statistics trellis termination bit error probability

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VLSI architectural design tradeoffs for sliding-window Log-MAP decoders

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2005年第4期13卷 439-447页

作者： Wu, CM Shieh, MD Wu, CH Hwang, YT Chen, JH Natl Appl Res Labs CIC Hsinchu 300 Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan Natl Chung Cheng Univ Dept Elect Engn Chiayi 621 Taiwan Natl Yunlin Univ Sci & Technol Dept Elect Engn Yunlin 640 Taiwan

Turbo codes have received tremendous attention and have commenced their practical applications due to their excellent error-correcting capability. Investigation of efficient iterative decoder realizations is of particular interest because the underlying soft-input soft-output decoding algorithms usually lead to highly complicated implementation. This paper describes the architectural design and analysis of sliding-window (SW) Log-MAP decoders in terms of a set of predetermined parameters. The derived mathematical representations can be applied to construct a variety of VLSI architectures for different applications. Based on our development, a SW-Log-MAP decoder complying with the specification of third-generation mobile radio systems is realized to demonstrate the performance tradeoffs among latency, average decoding rate, area/computation complexity, and memory power consumption. This paper thus provides useful and general information on practical implementation of SW-Log-MAP decoders.

关键词： iterative decoding MAP algorithm sliding window algorithm turbo code VLSI architecture and design

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Online Traffic Prediction in the Cloud: A Dynamic window Approach 2

Online Traffic Prediction in the Cloud: A Dynamic Window App...

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2nd International Conference on Future Internet of Things and Cloud (FiCloud)

作者： Dalmazo, Bruno L. Vilela, Joao P. Curado, Marilia Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

ISBN: (纸本)9781479943579

Traffic prediction is a fundamental tool that captures the inherent behavior of a network and can be used for monitoring and managing network traffic. Online traffic prediction is usually performed based on large historical data used in training algorithms. This may not be suitable to highly volatile environments, such as cloud computing, where the coupling between observations decreases quickly with time. We propose a dynamic window size approach for traffic prediction that can be incorporated with different traffic predictions mechanisms, making them suitable to online traffic prediction by adapting the amount of traffic that must be analyzed in accordance to the variability of data traffic. The evaluation of the proposed solution is performed for several prediction mechanisms by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of predicted values over observed values from a real cloud computing data set, collected by monitoring the utilization of Dropbox.

关键词： Cloud computing network traffic prediction short-range dependence sliding window algorithm

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Identification of genes associated with resistance trait to VP AHPND in Pacific white shrimp ( Litopenaeus vannamei ) using Bulk Segregation Analysis (BSA)

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Aquaculture Reports 2025年 43卷

作者： Zhenning Bao Yang Yu Pengfei Lin Fuhua Li State Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture Institute of Oceanology Chinese Academy of Sciences Qingdao 266000 China University of Chinese Academy of Sciences Beijing 100049 China Laboratory for Marine Biology and Biotechnology Qingdao Marine Science and Technology Center Qingdao 266071 China

Vibrio parahaemolyticus harboring the PirA/PirB toxin (VP AHPND ) is regarded as one of the main pathogens that caused Acute hepatopancreatic necrosis disease (AHPND) in the Pacific white shrimp Litopenaeus vannamei ( L. vannamei ). Breeding new varieties resistant to VP AHPND is considered to be the most effective way to reduce the economic losses in shrimp aquaculture. Identification of genes and SNP markers associated with resistance trait could accelerate the breeding efficiency by application of marker assisted selection and genomic selection. In the present study, a Bulk Segregation Analysis coupled to whole genome sequencing method was employed to identify SNPs and genes associated with resistance trait to VP AHPND in L. vannamei . The DNA of susceptible and resistant individuals was pooled separately. ED (Euclidean distance) and the chi-square test were applied to calculate the allele frequency and genotype frequency differences between susceptible and resistant individuals. A total of 2238 SNPs and 97 genes were identified to be associated with resistance trait to VP AHPND . KEGG enrichment analysis showed that these genes were significantly enriched in PPAR signaling pathway, PI3K-Akt signaling pathway, NLR pathway and O-Antigen nucleotide sugar biosynthesis pathway. The acyl-CoA delta-9 desaturase gene, 14–3–3 epsilon-like gene, retrovirus-related Pol polyprotein gene, enzymatic polyprotein gene in these pathways were considered as candidate genes associated with resistance trait to VP AHPND of shrimp. This study demonstrated that BSA based on whole genome re-sequencing was a cost-effective method for identifying disease resistant genes in aquaculture animals. These data will not only provide useful information for understanding the genetic basis of shrimp resistance trait to VP AHPND , but also offer a source of SNPs for marker-assisted selection and genomic selection in shrimp breeding.

关键词： Litopenaeus vannamei Bulk Segregation Analysis (BSA) Resistance to VP AHPND Single nucleotide polymorphisms (SNPs) sliding window algorithm

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Prediction of COVID-19 spread by sliding mSEIR observer

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Science China(Information Sciences) 2020年第12期63卷 166-178页

作者： Duxin CHEN Yifan YANG Yifan ZHANG Wenwu YU Jiangsu Key Laboratory of Networked Collective Intelligence School of Mathematics Southeast University School of Information Science and Engineering Southeast University

The outbreak of COVID-19 has brought unprecedented challenges not only in China but also in the whole world. Thousands of people have lost their lives, and the social operating system has been affected seriously. Thus, it is urgent to study the determinants of the virus and the health conditions in specific populations and to reveal the strategies and measures in preventing the epidemic spread. In this study, we first adopt the long short-term memory algorithm to predict the infected population in China. However,it gives no interpretation of the dynamics of the spread process. Also the long-term prediction error is too large to be accepted. Thus, we introduce the susceptible-exposed-infected-removed(SEIR) model and further the metapopulation SEIR(mSEIR) model to capture the spread process of COVID-19. By using a sliding window algorithm, we suggest that the parameter estimation and the prediction of the SEIR populations are well performed. In addition, we conduct extensive numerical experiments to show the trend of the infected population for several provinces. The results may provide some insight into the research of epidemics and the understanding of the spread of the current COVID-19.

关键词： epidemic spread prediction sliding window algorithm COVID-19

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TRAFFIC DESCRIPTORS FOR VBR VIDEO TELECONFERENCING OVER ATM NETWORKS

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IEEE-ACM TRANSACTIONS ON NETWORKING 1995年第3期3卷 329-339页

作者： REIBMAN, AR BERGER, AW AT&T Bell Labs. Holmdel NJ USA

This paper examines the problem of video;transport over ATM networks using knowledge of both video system design and broadband networks, The following issues are addressed: video system delay caused by internal buffering, traffic descriptors (TD) for video, and call admission, We find that while different video sequences require different TD parameters, the following trends hold for all sequences examined, First, increasing the delay in the video system decreases the necessary peak rate and significantly increases the number of calls that can be carried by the network, Second;as ah operational traffic descriptor for video, the leaky-bucket algorithm appears to be superior to the sliding-window algorithm, And finally, with a delay in the video system, the statistical multiplexing gain from VER over CBR video is upper bounded by roughly a factor of four, and to obtain a gain of about 2.0 can require the operational traffic descriptor to have a window or bucket size on the order of a thousand cells. We briefly discuss how increasing the complexity of the video system may enable the size of the bucket or window to be reduced.

关键词： Asynchronous Transfer Mode Telecommunication Traffic B ISDN Teleconferencing Delays Buffer Storage VBR Video Teleconferencing ATM Networks Traffic Descriptors Video Transport Video System Design Broadband Networks Video System Delay Internal Buffering Call Admission Video Sequences Peak Rate Leaky Bucket algorithm sliding window algorithm Statistical Multiplexing Gain CBR Video Bucket Size window Size Variable Bit Rate Video Constant Bit Rate Video B ISDN Telecommunication Traffic Teleconferencing Asynchronous Transfer Mode Communication System Traffic Control B ISDN Quality Of Service Control Systems Video Sharing Delay Systems Added Delay

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Online traffic prediction in the cloud

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INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT 2016年第4期26卷 29-45页

作者： Dalmazo, Bruno L. Vilela, Joao P. Curado, Marilia Univ Coimbra Dept Informat Engn CISUC Coimbra Portugal

Network traffic prediction is a fundamental tool to harness several management tasks, such as monitoring and managing network traffic. Online traffic prediction is usually performed based on large sets of historical data used in training algorithms, for example, to determine the size of static windows to bound the amount of traffic under consideration. However, using large sets of historical data may not be suitable in highly volatile environments, such as cloud computing, where the coupling between time series observations decreases rapidly with time. To fill this gap, this work presents a dynamic window size algorithm for traffic prediction that contains a methodology to optimize a threshold parameter alpha that affects both the prediction and computational cost of our scheme. The alpha parameter defines the minimum data traffic variability needed to justify dynamic window size changes. Thus, with the optimization of this parameter, the number of operations of the dynamic window size algorithm decreases significantly. We evaluate the alpha estimation methodology against several prediction models by assessing the normalized mean square error and mean absolute percent error of predicted values over observed values from two real cloud computing datasets, collected by monitoring the utilization of Dropbox, and a data center dataset including traffic from several common cloud computing services. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： cloud computing network traffic prediction short-range dependence sliding window algorithm

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Impulse fault classification in transformers by fractal analysis

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 2003年第1期10卷 109-116页

作者： Purkait, P Chakravorti, S Haldia Inst Technol Instrumentat Engn Dept Haldia 721657 Midnapore India Jadavpur Univ Dept Elect Engn Kolkata 700032 India

Transformers are usually subjected to lightning impulse tests after assembly for assessment of their insulation strength. In the case of a fault the resulting winding current gets changed to a certain extent. The pattern of the fault currents depends on the type of fault and its location along the length of the winding. This paper describes the application of the concept of fractal geometry to analyze the properties of fault currents. Fractal features such as fractal dimension, lacunarity used for image surface recognition and the sliding window algorithm used for fractal analysis of waveform have been employed for classification of transformer impulse faults. Experimental results obtained for a 3 MVA transformer and simulation results obtained for 3 MVA, 5 MVA and 7 MVA transformers are presented to illustrate the ability of this approach to classify insulation failures. The results indicate that this new approach possesses reasonable abilities for waveform pattern discrimination.

关键词： transformers insulation impulse fault fault classification fault simulation fractal dimensions lacunarity sliding window algorithm

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An improved global grid model for calibrating zenith tropospheric delay for GNSS applications

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GPS SOLUTIONS 2023年第1期27卷 17页

作者： Huang, Liangke Zhu, Ge Peng, Hua Liu, Lilong Ren, Chao Jiang, Weiping Guilin Univ Technol Coll Geomat & Geoinformat Guilin 541004 Peoples R China Guangxi Key Lab Spatial Informat & Geomat Guilin 541004 Peoples R China Wuhan Univ GNSS Res Ctr Wuhan 430079 Peoples R China

Accurate modeling of zenith tropospheric delay (ZTD) is beneficial for high-precision navigation and positioning. Many models with good performance have been developed for calibrating ZTD, such as the GPT3 model, which is recognized as an excellent global model and is widely used. However, certain limitations still remain in current models, such as the adoption of only single gridded data for modeling, and the model parameters need to be further optimized. In our previous research, a new approach based on the sliding window algorithm was proposed and applied to develop the GZTD-H model to address some of these limitations. However, this model is only suitable for the vertical adjustment of ZTD, not for estimating ZTD directly. In this study, an improved global grid ZTD model considering height scale factor (GGZTD-H) is derived from the initial GZTD-H model for estimating ZTD. The RMSs of the GGZTD-H model are 4.11 cm and 3.29 cm as validated by radiosonde data and IGS data, respectively. Compared with the UNB3m model and the canonical GPT3 model, the new model exhibits better performance. Moreover, three resolutions of the GGZTD-H model have been developed to reduce the quantity of gridded data delivered to users and optimize the ZTD computation process. Compared with the GPT3 model, the GGZTD-H model shows better performance with lower resolution and requires fewer model parameters for ZTD estimation, greatly optimizing ZTD computation. Users may select the best model that meets their needs in terms of the balance between resolution and accuracy. The high-precision GGZTD-H model could be used as a ZTD vertical stratification model for the vertical adjustment of atmospheric data and as an empirical model for ZTD estimation, which has potential applications in GNSS precise positioning, such as for the establishment and maintenance of the global terrestrial reference frame.

关键词： Zenith tropospheric delay Global model sliding window algorithm Global navigation satellite system (GNSS) Vertical adjustment

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A framework for ship abnormal behaviour detection and classification using AIS data

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2024年 247卷

作者： Rong, H. Teixeira, A. P. Soares, C. Guedes Univ Nova Lisboa Inst Super Tecn Ctr Marine Technol & Ocean Engn CENTEC Lisbon Portugal

This paper proposes a method for detecting and classifying ship abnormal behaviour in ship trajectories. The method involves generating parameter profiles for the ship's trajectory and applying a sliding window algorithm to detect the ship's abnormal behaviour. Then, several features are adopted to effectively describe the characteristics of each ship's abnormal behaviour, such as the standard deviation of speed, detour factor, maximum drift angle, accumulative change of Course Over Ground and maximum lateral distance to the ship route. A density -based clustering algorithm is applied to group similar abnormal behaviour patterns according to the feature similarity, and the Random Forest Classification method is used to train a classification model based on the features extracted from the clusters. The proposed method is then tested on historical ship trajectory data provided by the Automatic Identification System. The results suggest that the method effectively identifies and classifies different abnormal behaviours in the ship trajectories.

关键词： Maritime Traffic AIS data Ship abnormal behaviour sliding window algorithm Feature extraction Random forest classification

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