Analysis on Markov modeling of cellular packet transmission

2003 ◽  
Author(s):  
Danlu Zhang ◽  
Wei Biao Wu ◽  
K.M. Wasserman
Keyword(s):  
Packet Transmission ◽  
Markov Modeling

scholarly journals MODER2: first-order Markov modeling and discovery of monomeric and dimeric binding motifs

2020 ◽  
Vol 36 (9) ◽  
pp. 2690-2696
Author(s):  
Jarkko Toivonen ◽  
Pratyush K Das ◽  
Jussi Taipale ◽  
Esko Ukkonen
Keyword(s):  
Markov Models ◽  
Expectation Maximization Algorithm ◽  
Software Tool ◽  
Specific Weight ◽  
Training Data ◽  
Supplementary Information ◽  
Markov Modeling ◽  
Binding Motifs ◽  
The Difference ◽  
Probability Matrices

Abstract Motivation Position-specific probability matrices (PPMs, also called position-specific weight matrices) have been the dominating model for transcription factor (TF)-binding motifs in DNA. There is, however, increasing recent evidence of better performance of higher order models such as Markov models of order one, also called adjacent dinucleotide matrices (ADMs). ADMs can model dependencies between adjacent nucleotides, unlike PPMs. A modeling technique and software tool that would estimate such models simultaneously both for monomers and their dimers have been missing. Results We present an ADM-based mixture model for monomeric and dimeric TF-binding motifs and an expectation maximization algorithm MODER2 for learning such models from training data and seeds. The model is a mixture that includes monomers and dimers, built from the monomers, with a description of the dimeric structure (spacing, orientation). The technique is modular, meaning that the co-operative effect of dimerization is made explicit by evaluating the difference between expected and observed models. The model is validated using HT-SELEX and generated datasets, and by comparing to some earlier PPM and ADM techniques. The ADM models explain data slightly better than PPM models for 314 tested TFs (or their DNA-binding domains) from four families (bHLH, bZIP, ETS and Homeodomain), the ADM mixture models by MODER2 being the best on average. Availability and implementation Software implementation is available from https://github.com/jttoivon/moder2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals On rate control of packet transmission over fading channels

2007 ◽  
Vol 11 (12) ◽  
pp. 982-984 ◽  
Author(s):  
Jie Luo ◽  
Anthony Ephremides
Keyword(s):  
Fading Channels ◽  
Rate Control ◽  
Packet Transmission

Deriving Effective Decision-Making Strategies of Prosthetists: Using Hidden Markov Modeling and Qualitative Analysis to Compare Experts and Novices

2021 ◽  
pp. 001872082110328
Author(s):  
Pratima Saravanan ◽  
Jessica Menold
Keyword(s):  
Decision Making ◽  
Qualitative Analysis ◽  
Clinical Decision Making ◽  
Hidden Markov ◽  
Activity Level ◽  
Markov Modeling ◽  
Support Tool ◽  
Case Complexity ◽  
Hidden Markov Modeling ◽  
Expert And Novice

Objective This research focuses on studying the clinical decision-making strategies of expert and novice prosthetists for different case complexities. Background With an increasing global amputee population, there is an urgent need for improved amputee care. However, current prosthetic prescription standards are based on subjective expertise, making the process challenging for novices, specifically during complex patient cases. Hence, there is a need for studying the decision-making strategies of prosthetists. Method An interactive web-based survey was developed with two case studies of varying complexities. Navigation between survey pages and time spent were recorded for 28 participants including experts ( n = 20) and novices ( n = 8). Using these data, decision-making strategies, or patterns of decisions, during prosthetic prescription were derived using hidden Markov modeling. A qualitative analysis of participants’ rationale regarding decisions was used to add a deep contextualized understanding of decision-making strategies derived from the quantitative analysis. Results Unique decision-making strategies were observed across expert and novice participants. Experts tended to focus on the personal details, activity level, and state of the residual limb prior to prescription, and this strategy was independent of case complexity. Novices tended to change strategies dependent upon case complexity, fixating on certain factors when case complexity was high. Conclusion The decision-making strategies of experts stayed the same across the two cases, whereas the novices exhibited mixed strategies. Application By modeling the decision-making strategies of experts and novices, this study builds a foundation for development of an automated decision-support tool for prosthetic prescription, advancing novice training, and amputee care.

Research on MANET Routing Algorithm Based on Ant Colony Algorithm

2011 ◽  
Vol 135-136 ◽  
pp. 781-787
Author(s):  
Yong Feng Ju ◽  
Hui Chen
Keyword(s):  
Network Lifetime ◽  
Ant Colony Algorithm ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Packet Transmission ◽  
Ant Colony ◽  
Dynamic Allocation ◽  
Routing Table ◽  
Manet Routing ◽  
Rate Dynamic

This paper proposed a new Ad Hoc dynamic routing algorithm, which based on ant-colony algorithm in order to reasonably extend the dynamic allocation of network traffic and network lifetime. The Algorithm choose path according transmission latency, path of the energy rate, congestion rate, dynamic rate. The Algorithm update the routing table by dynamic collection of path information after path established. The analyse shows that algorithm increases the network throughput, reduces the average end-to-end packet transmission latency, and extends the network lifetime, achieves an improving performance.

scholarly journals A Guideline on Pseudorandom Number Generation (PRNG) in the IoT

2021 ◽  
Vol 54 (6) ◽  
pp. 1-38
Author(s):  
Peter Kietzmann ◽  
Thomas C. Schmidt ◽  
Matthias Wählisch
Keyword(s):  
General Purpose ◽  
Packet Transmission ◽  
Limited Resources ◽  
Systematic Evaluation ◽  
The Internet ◽  
Random Numbers ◽  
Essential Input ◽  
Attack Surface ◽  
Pseudorandom Number Generation ◽  
The Internet Of Things

Random numbers are an essential input to many functions on the Internet of Things (IoT). Common use cases of randomness range from low-level packet transmission to advanced algorithms of artificial intelligence as well as security and trust, which heavily rely on unpredictable random sources. In the constrained IoT, though, unpredictable random sources are a challenging desire due to limited resources, deterministic real-time operations, and frequent lack of a user interface. In this article, we revisit the generation of randomness from the perspective of an IoT operating system (OS) that needs to support general purpose or crypto-secure random numbers. We analyze the potential attack surface, derive common requirements, and discuss the potentials and shortcomings of current IoT OSs. A systematic evaluation of current IoT hardware components and popular software generators based on well-established test suits and on experiments for measuring performance give rise to a set of clear recommendations on how to build such a random subsystem and which generators to use.

Sign in / Sign up

Export Citation Format

Share Document