Efficient estimation algorithm for ARMA, exponential and other trigonometric model with quantum constraints

2009 ◽  
Vol 3 (1) ◽  
pp. 64 ◽  
Author(s):  
S. Sasikumar ◽  
S. Karthikeyan ◽  
M. Suganthi ◽  
M. Madheswaran
Keyword(s):  
Estimation Algorithm ◽  
Efficient Estimation ◽  
Trigonometric Model ◽  
Quantum Constraints

An Energy Efficient Estimation Algorithm for Wireless Sensor Network

2011 ◽  
Vol 216 ◽  
pp. 176-180
Author(s):  
Yong Ding ◽  
Yue Mei Su
Keyword(s):  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Network Lifetime ◽  
Energy Efficient ◽  
Estimation Algorithm ◽  
Efficient Estimation ◽  
Wireless Sensor ◽  
Estimation Algorithms ◽  
Current Energy

Wireless Sensor Networks functionality is closely related to network lifetime which depends on the energy consumption, so require energy- efficient protocols to improve the network lifetime. According to the analysis and summary of the current energy efficient estimation algorithms in wireless sensor network An energy-efficient algorithm is proposed,. Then this optimization algorithm proposed in the paper is adopted to improve the traditional diffusion routing protocol. Simulation results show that this algorithm is to effectively balance the network energy consumption, improve the network life-cycle and ensure the communication quality.

scholarly journals Conditional Analysis for Mixed Covariates, with Application to Feed Intake of Lactating Sows

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
S. Y. Park ◽  
C. Li ◽  
S. M. Mendoza Benavides ◽  
E. van Heugten ◽  
A. M. Staicu
Keyword(s):  
Feed Intake ◽  
Dynamic Change ◽  
Estimation Algorithm ◽  
Joint Estimation ◽  
Efficient Estimation ◽  
Specific Information ◽  
Computationally Efficient ◽  
Modeling Framework ◽  
Lactating Sows ◽  
Information Vector

We propose a novel modeling framework to study the effect of covariates of various types on the conditional distribution of the response. The methodology accommodates flexible model structure, allows for joint estimation of the quantiles at all levels, and provides a computationally efficient estimation algorithm. Extensive numerical investigation confirms good performance of the proposed method. The methodology is motivated by and applied to a lactating sow study, where the primary interest is to understand how the dynamic change of minute-by-minute temperature in the farrowing rooms within a day (functional covariate) is associated with low quantiles of feed intake of lactating sows, while accounting for other sow-specific information (vector covariate).

scholarly journals A Fast and Accurate Method for Estimating People Flow from Spatiotemporal Population Data

2018 ◽  
Author(s):  
Yasunori Akagi ◽  
Takuya Nishimura ◽  
Takeshi Kurashima ◽  
Hiroyuki Toda
Keyword(s):  
Graphical Models ◽  
Degrees Of Freedom ◽  
Traditional Approach ◽  
Transition Probability ◽  
Population Data ◽  
Estimation Algorithm ◽  
Efficient Estimation ◽  
Estimation Accuracy ◽  
Computation Cost ◽  
Crowd Movement

Real-time spatiotemporal population data is attracting a great deal of attention for understanding crowd movements in cities.The data is the aggregation of personal location information and consists of just areas and the number of people in each area at certain time instants. Accordingly, it does not explicitly represent crowd movement. This paper proposes a probabilistic model based on collective graphical models that can estimate crowd movement from spatiotemporal population data. There are two technical challenges: (i) poor estimation accuracy as the traditional approach means the model would have too many degrees of freedom, (ii) excessive computation cost. Our key idea for overcoming these two difficulties is to model the transition probability between grid cells (cells hereafter) in a geospatial grid space by using three factors: departure probability of cells, gathering score of cells, and geographical distance between cells. These advances enable us to reduce the degrees of freedom of the model appropriately and derive an efficient estimation algorithm.  To evaluate the performance of our method, we conduct experiments using real-world spatiotemporal population data. The results confirm the effectiveness of our method, both in estimation accuracy and computation cost.   

scholarly journals Rapid classification of hippocampal replay content for real-time applications

2016 ◽  
Vol 116 (5) ◽  
pp. 2221-2235 ◽  
Author(s):  
Xinyi Deng ◽  
Daniel F. Liu ◽  
Mattias P. Karlsson ◽  
Loren M. Frank ◽  
Uri T. Eden
Keyword(s):  
Real Time ◽  
Learning And Memory ◽  
Cell Activity ◽  
Memory Task ◽  
Estimation Algorithm ◽  
Place Cell ◽  
Efficient Estimation ◽  
Memory Impairments ◽  
Software And Hardware

Sharp-wave ripple (SWR) events in the hippocampus replay millisecond-timescale patterns of place cell activity related to the past experience of an animal. Interrupting SWR events leads to learning and memory impairments, but how the specific patterns of place cell spiking seen during SWRs contribute to learning and memory remains unclear. A deeper understanding of this issue will require the ability to manipulate SWR events based on their content. Accurate real-time decoding of SWR replay events requires new algorithms that are able to estimate replay content and the associated uncertainty, along with software and hardware that can execute these algorithms for biological interventions on a millisecond timescale. Here we develop an efficient estimation algorithm to categorize the content of replay from multiunit spiking activity. Specifically, we apply real-time decoding methods to each SWR event and then compute the posterior probability of the replay feature. We illustrate this approach by classifying SWR events from data recorded in the hippocampus of a rat performing a spatial memory task into four categories: whether they represent outbound or inbound trajectories and whether the activity is replayed forward or backward in time. We show that our algorithm can classify the majority of SWR events in a recording epoch within 20 ms of the replay onset with high certainty, which makes the algorithm suitable for a real-time implementation with short latencies to incorporate into content-based feedback experiments.

The Statistical Evaluations of Transmission Characteristics, Limits of Ranges and Speeds of Transmission of Information Via the Pulsed Atmospheric Bistatic Optical Channels

2019 ◽  
pp. 97-104
Author(s):  
Mikhail V. Tarasenkov ◽  
Egor S. Poznakharev ◽  
Vladimir V. Belov
Keyword(s):  
Communication Systems ◽  
Estimation Algorithm ◽  
Transmission Characteristics ◽  
Radiation Beam ◽  
Optical Channels ◽  
The Monte Carlo Method ◽  
Wide Range ◽  
Transmission Of Information ◽  
Optical Axes ◽  
Visibility Range

The simulation program by the Monte Carlo method of pulse reactions of bistatic atmospheric aerosol-gas channels of optical-electronic communication systems (OECS) is created on the basis of the modified double local estimation algorithm. It is used in a series of numerical experiments in order to evaluate statistically the transfer characteristics of these channels depending on the optical characteristics of an atmosphere plane-parallel model for wavelengths λ = 0.3, 0.5, and 0.9 μm at a meteorological visibility range SM = 10 and 50 km. The results are obtained for a set of basic distances between the light source and the light receiver up to 50 km and for the angular orientations of the optical axes of a laser radiation beam and of the receiving system in a wide range of their values. The dependences of the pulse reactions maximum values over-the-horizon channels of the OECS on the variations of these parameters are established.

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