А. В. Богатырев, В. А. Богатырев, S. V. Bogatyrev · 2019 Wave Electronics and its Application in Information and Telecommunication Systems (WECONF) · 2019
Models are proposed and the impact of fragmentation and redundancy of transmissions on the average waiting time for requests and the probability of their timely service during multipath transmission of delays that are critical for network delays are analyzed. The effectiveness of redundant transmissions in the fragmentation of transmitted packets is shown. Established the existence of optimal fragmentation and multiplicity of redundancy transmissions of packets that are critical to the time of their delivery.
Cited by 19
GÓRECKI P, CAPONETTI L, CASTIELLO, CIRO · CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) · 2007
Cited by 0
Ken Christensen, Kenji Yoshigoe, Allen Roginsky et al. · 2004
Most input buffered packet switches internally segment variable-length packets into fixed-length cells. The last cell in a segmented packet contains overhead bytes if the packet length is not evenly divisible by the cell length. Switch speed-up is used to compensate for this overhead. In this paper, we develop an analytical model of a single-server queue where an input stream of packets is segmented into cells for service. Analytical models are developed for M/M/1, M/H/sub 2//1, and M/E/sub 2//1 queues with a discretized (or quantized) service time. These models and simulation using real packet traces are used to evaluate the effect of speed-up on mean queue length. We propose and evaluate a new method of segmenting a packet trailer and subsequent packet header into a single cell. This cell merging method reduces the required speed-up. No changes to switch-matrix scheduling algorithms are needed. Simulation with a packet trace shows a reduction in the needed speed-up for an iSLIP scheduled input buffered switch.
Cited by 9
Tripti Singh, Abhishek Misal · 2015
Murmurs are the heart sounds which are produced due to abnormal flow of blood through it. Phonocardiogram (PCG) is a highly non-stationary signal and is used for the auscultation purpose for detection of heart disease. For detection and analysis purpose, PCG signal is segmented or classified into main heart sound components S1 and S2. The segmentation process used in this paper is carried out through wavelet packet for deeper analysis and also signal is represented in time and frequency domain. The wavelets are mathematical tools which cut the signal into various frequency components. This wavelet packet decomposition is for richer signal analysis. The improved method for signal analysis can be developed using this tool which has the potential of removing background noise produced during recording and diagnosis. Keywords: Discrete wavelet transform, normalized root mean square error, phonocardiographic (PCG) signal, peak signal to noise ratio, S1, S2, wavelet packet transform Cite this Article Singh Tripti, Misal Abhishek. Implementation of Wavelet Packet for PCG Signal Segmentation. Research and Reviews: Journal of Computational Biology . 2015; 5(3): 1–6p.
Cited by 0
R. Morera, J. Dunlop · 2003
This paper proposes the combination of packet segmentation and a polling scheme for data scheduling to overcome unfairness in a mobile radio TDMA system. A comprehensive study of the impact of segment size and source characteristics on the performance of this scheme has been carried out.
Cited by 1
Shuai You, Shijun Lin, Yujian Feng et al. · Preprints.org · 2024
The segmentation of abnormal regions is vital in smart manufacturing. However, the existing segmentation system for detecting sauce-packet leakage on intelligent sensors encounters an issue of imaging blurring caused by uneven illumination. This issue adversely affects segmentation performance, thereby impeding the rapid production of industrial assembly lines. To alleviate this issue, we propose the two-stage Illumination-aware Sauce-packet Leakage Segmentation (ISLS) method for intelligent sensors. The ISLS comprises two main stages: Illumination-aware region enhancement and leakage region segmentation. In the first stage, YOLO-Fastestv2 is employed to capture the Region of Interest (ROI), which reduces redundancy computations. Additionally, we propose an image enhancement to relieve the impact of uneven illumination, enhancing the texture details of ROI. In the second stage, we propose a novel feature extraction network. Specifically, we propose the Multi-scale Feature Fusion Module (MFFM) and the Sequential Self-Attention Mechanism (SSAM) to capture discriminative representations of leakage. The multi-level features are fused by MFFM with a small number of parameters, which capture leakage semantics at different scales. The SSAM realizes the enhancement of valid features and the suppression of invalid features by adaptive weighting of spatial and channel dimensions. Furthermore, we generated a self-built dataset of sauce-packets, including 606 images with various leakage areas. Comprehensive experiments demonstrate that our ISLS method shows better results than several state-of-the-art methods, with additional performance analyses deployed on intelligent sensors to affirm the effectiveness of our proposed method. Our code is available at https://github.com/LSJ5106/SauceDetect.
Cited by 1Open access
Petr Dokládal, C. Braun, R. Rabi · 2002
In many mixed-technology packet switching networks, segmentation is required to convert larger data units of one technology (e.g., frame relay) to data units of another technology (e.g., asynchronous transfer mode or ATM). This paper examines cut-through and non-cut-through segmentation and presents simulation results of delay performance for these two options for a network consisting of local access arrangements, frame relay network access links, and an ATM backbone network, cut-through segmentation is found to significantly reduce delays when the segmenter input blocks are large compared to the output segments and the segmenter input clock rate is small compared to other network rates. Cut-through segmentation also typically produces a less bursty output stream which improves queuing delay performance for all network traffic. In our sample network, the effect of the different segmentation options is found to be highly dependent on the local access arrangement.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Cited by 1
Daniel Llorente, Kimon Karras, Thomas Wild et al. · Lecture notes in computer science · 2011
Cited by 0
Mnassar Alyami, Cliff C. Zou, Yan Solihin · 2024
Connected Internet-of- Things (IoT) devices pose sev-eral privacy risks through the analysis of their encrypted network traffic. According to prior studies, packet size can be utilized to train a machine learning classifier for the identification of IoT de-vices because of their unique functionalities and traffic patterns. A recent defense technique aimed at addressing these privacy concerns efficiently is random segmentation [1]. This mechanism involves breaking down application messages into randomly sized chunks to obscure patterns in packet sizes. However, it leads to higher latency due to the increased number of packets and the additional packet header overhead. Furthermore, nonadaptive (or static) splitting in the original random segmentation approach is inappropriate for networks with dynamically changing conditions, which is common in smart homes. In this paper, we present an adaptive segmentation approach based on optimization, which adapts the splitting volume to changes in network usage. We formulate an optimization problem in order to maximize network traffic obfuscation while minimizing segmentation overhead. We evaluated our adaptive approach through simulations using real-world IoT data traces. Our results illustrate how the adaptive defense system adjusts its splitting parameters to enhance privacy protection, as measured by entropy, while minimizing the impact on transmission performance.
Cited by 1
Mausumi Acharyya, Malay K. Kundu · 2007
Abstract — The present article describes a image segmentation technique using M-band wavelet packet frames features. Those wavelet features are evaluated and selected using an efficient neuro-fuzzy feature evaluation techniqu. Both the feature extraction and neuro-fuzzy feature evaluation processes are of unsupervised type. They do not require the knowledge of number and distribution of classes corresponding to various landcovers in remotely sensed images. The effectiveness of the methodology is demonstrated on IRS-1A and SPOT images. Copyright c ○ 2007 Yang’s Scientific Research Institute, LLC. All rights reserved. Index Terms — M-band wavelet packet frames, adaptive basis selection, fuzzy feature evaluation index, neural networks, remotely sensed image I.
Cited by 21
Zhitao Li, Min Yan, Huimin Xu · 2008
Today's IEEE 802.11 Wireless LAN (WLAN) is an excellent solution for the broadband wireless networking. However, it lacks of the capability to support real-time services such as Voice-over-IP (VoIP) properly. In this paper, a novel link-layer packet segmentation method is proposed to promote the performance of VoIP which do not influence non-real-time application much. In this method the channel utilization is used to decide the factor of the segmentation. This method is verified through simulations.
Cited by 0
Ying Li, Yanning Zhang, Xiaoyue Jiang et al. · 2008
The presence of speckle in Synthetic Aperture Radar (SAR) images makes the segmentation of such images difficult A novel method for automatic segmentation of SAR images is proposed. Firstly, a wavelet packet based texture feature set is derived. It consists of the energy of the feature subimages obtained by the overcomplete wavelet packet decomposition of local areas in SAR image, where the downsampling between wavelet levels is omitted. Then an improved unsupervised clustering algorithm is proposed for image segmentation, which can determine the number of classes automatically. Segmentation results on real SAR image demonstrate the effectiveness of the proposed method.
Cited by 0
FengHua Xu, Kai Wang, Yukun Zhu et al. · 2023
Censorship evasion has been a long-standing game between evaders and censors in cybersecurity and has evolved from manual evasion to automated evasion using complex protocol information. However, censorship techniques like deep packet inspection and machine learning-based traffic identification techniques have undergone dynamic upgrades. In this paper, we build a prototype genetic algorithm system for packet segmentation and automated censorship circumvention , SegmentGeneva. Specifically, we combine the advantages of packet segmentation operations, which cause incomplete content recognition by censors, difficulties in large-scale packet reorganization, and high resource consumption, with the relevant points of packet segmentation features in TCP/IP protocols and traffic censorship techniques, automated exploration and evolution of genetic algorithms for packet manipulation policy reasoning. The packet operation policies that can break through the firewall are generated by sending packet requests containing sensitive fields to the border firewall and automatically optimizing them according to the different response results. Fifty-one packet operation policies with strong evasion capabilities are obtained, and the final results are analyzed and compared. The system can effectively explore the censor’s censorship mechanism for packet segmentation traffic and evolve excellent censorship evasion strategies based on the response results of automated exploration.
Cited by 0
Switched Networks Vinod, Vinod M. Vokkarane, Jason P. Jue et al. · 2002
In this paper we address the issue of contention resolution in optical burst switched networks, and we introduce an approach for reducing packet losses which is based on the concept of burst segmentation. In burst segmentation, rather than dropping the entire burst during contention, the burst may be broken into multiple segments, and only the overlapping segments are dropped. The segmentation scheme is investigated in conjunction with a deflection scheme through simulation, and it is shown that segmentation with deflection can achieve a significantly reduced packet loss rate.
Cited by 0
Lina Abou-Abbas, Hesam Fersai Alaei, Chakib Tadj · 2015
This paper proposes a method for the segmentation of newborn's cry signals recorded in real conditions using the Teager-Kaiser energy operator (TKEO). Based on the wavelet packet analysis, the audio signals are divided into different frequency channels, and then the TKEO and the energy are estimated within each band. The Hidden Markov Models have been used as a classification tool to distinguish the voiced cry parts from the irrelevant acoustic activities that compose the audio signals. The proposed method divided the audio signal containing newborns' cry sounds into different periods showing the audible Expiration and Inspiration of the cry. Different levels of wavelet packet transform have been used to verify the performance of the proposed method on crying signals segmentation and have shown that based on wavelet packet decomposition, the TKEO measure is more effective than the traditional energy measure in detecting important parts of cry signal in a very noisy environment. The proposed features have shown to achieve an accuracy rate of 84.08 %.
Cited by 5
Kimon Karras, Daniel Llorente, Thomas Wild et al. · 2008
Network processing requirements are climbing at an impressive rate. The introduction of state-of-the-art, high-speed optical links puts additional strain on all electronic components of a network processor. This is particularly true for the memory subsystem, since the traditional performance gap between memory and processor is also present there. A major contribution to improve the performance of the memory subsystem is to define an optimized algorithm for segmenting network packets. The goal is to ensure a minimum number of segments, so as to achieve maximum packet throughput, while maintaining a high level of memory efficiency. In this paper we present two different algorithms, both of which utilize a variable number of segments from different sizes and evaluate them using a variety of stimuli and system configurations in order to determine how different architectural choices impact the performance of the algorithms and the system in general. The results verify that multiple segment size segmentation provides very high storing efficiency with a low number of segments per packet, thus allowing for significantly higher system throughput in comparison with currently used algorithms.
Cited by 1
Soo Chang Kim, Tae Jin Kang · 2015
In this paper, we propose a scheme for texture classification and segmentation. The methodology involves an extraction of texture features using the wavelet packet frame decomposition. This is followed by a Gaussian-mixture-based classifier which assigns each pixel to the class. Each subnet of the classifier is modeled by a Gaussian mixture model and each texture image is assigned to the class to which pixels of the image most belong. This scheme shows high recognition accuracy in the classification of Brodatz texture images. It can also be expanded to an unsupervised texture segmentation using a Kullback–Leibler divergence between two Gaussian mixtures. The proposed method was successfully applied to Brodatz mosaic image segmentation and fabric defect detection.
Cited by 0
Shadi AlZu’bi, Mhd Saeed Sharif, Maysam Abbod · 2011
3D volume segmentation aims at partitioning the voxels into 3D objects (sub-volumes) which represent meaningful physical entities. Multi-resolution analysis (MRA) allows for the preservation of an image according to certain levels of resolution or blurring. The quality of this approach makes it useful in image compression, de-noising, and classification or segmentation. This paper focuses on the implementation of a medical volume segmentation technique using different wavelet decompositions such as discrete wavelet transform (DWT) and discrete wavelet packet transform (WP). A comparison study has been carried out to evaluate both decompositions using different performance metrics such as Euclidean distance (ED) and dice similarity coefficients (DSC) which reveal that WP approaches can accurately detect the perform the details coefficients in both phantom and real data.
Cited by 25
K F Silva · LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2010
Orientador: Yuzo Iano
Cited by 0Open access
Soo Chang Kim, Tae Jin Kang · Pattern Recognition · 2006
Cited by 206