In this paper, we have proposed the ES-LMS/RLS and SI-LMS/RLS algorithms for distributed estimation in applications such as wireless sensor networks and smart grids.
Expectation Maximization (EM) is a popular approach for parameter estimation in many applications, such as image understanding, document classification, and genome data analysis.
This paper presents a novel hardware-friendly motion estimation for real-time applications such as robotics or autonomous navigation.
Recent advances in computational power have allowed the use of online parameter estimation techniques in varied applications such as reconfigurable or adaptive control, system health monitoring, and fault tolerant control.
Available bandwidth (ABW) estimation is useful for various applications such as network management, traffic engineering, and rate-based multimedia streaming.
The latter is critical in many applications, such as estimation of surface sensible and latent heat fluxes, energy budget, urban canopy modeling, bio-climatic studies and urban planning.
The αth inverse moment of (a+X_{n}) often plays an important role in many statistical applications such as Stein estimation, life testing problems, evaluating risks of estimators, evaluating the power of test statistics, financial and insurance mathematics, change-point analysis, and so on.
This in-season data can be useful for management applications such as crop estimation or fruit thinning.
There are also some other applications such as parameter estimation, prediction, and classification.
Retrieving and matching mechanical parts based on their shape has many applications such as cost estimation and process planning.
