Applications of DSP

Broesch, James D.

doi:10.1016/b978-0-7506-8976-2.00007-9

Cited by 7 publications

(5 citation statements)

References 0 publications

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“…In place of standard speckle-noise removal methods, filters specifically designed to remove granularity noise while preserving information pertaining to GA would be more suitable, such as the median filter. 22 Preserving retinal granularities could be useful in understanding the appearance and progression of lesions and even hyperfluorescent areas.…”

Section: Resultsmentioning

confidence: 99%

Progression of Geographic Atrophy: Epistemic Uncertainties Affecting Mathematical Models and Machine Learning

Arslan

Benke

2021

Trans. Vis. Sci. Tech.

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Section: Resultsmentioning

confidence: 99%

Progression of Geographic Atrophy: Epistemic Uncertainties Affecting Mathematical Models and Machine Learning

Arslan

Benke

2021

Trans. Vis. Sci. Tech.

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“…Fuzzy logic was first introduced in 1965 Lotfi Zadeh at University of California, Berkeley (Broesch, 2009;Passino et al, 1998). It is one of the most popular intelligent control techniques that convert the linguistic control strategy into an automation control strategy based on expert knowledge and fuzzy logic (Elnour & Taha, 2013).…”

Section: Fuzzy Controlmentioning

confidence: 99%

Application of different control algorithms on a ‘home-made’ temperature control lab kit

Nguyen

Quang

Pham

et al. 2022

CTUJS

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Providing enough facilities for students to do laboratory activities is important. An existing useful kit was proposed for students learning a variety of control engineering topics. A temperature control lab kit is made from scratch using common electronics components as a replacement for the original TCLab introduced by Hedengren (Hedengren et al., 2019). Mathematical models of the system derived from theoretical and experimental methods are simulated in Matlab/Simulink to verify their accuracy to the physical kit. Different control algorithms such as: On/Off, PID, Fuzzy are then applied on the Kit as well as its mathematical models to illustrate their control feasibility. Human machine interface (HMI) is also designed using Matlab GUI allowing an operator to select a control algorithm, tune control parameters and monitor parameters of the process. Experimental results show that the derived models can reflect quite well dynamics of the physical kit with temperature deviation among them in the range of ±3°C. This confirms that the kit is well-suited for teaching different control topics such as system modelling, system identification, classical control and advanced control algorithms.

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“…Although this CSI provides rich information from multiple subcarriers, it is sensitive to the change of environments such as human blocking and movements, interference from other equipment and devices, receiver antenna orientation, etc., [19]. In order to filter out the outliers caused by the interference and make the measurement more stable, we adopt the median filter [31] for every subcarrier (every column of an CSI image). Then an important normalization process is applied to get cleaner data from the original image.…”

Section: Proposed Localization Systemmentioning

confidence: 99%

A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization

Hoang¹,

Yuen²,

Ren³

et al. 2020

Preprint

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This paper proposes a combined network structure between convolutional neural network (CNN) and long-short term memory (LSTM) quantifier for WiFi fingerprinting indoor localization. In contrast to conventional methods that utilize only spatial data with classification models, our CNN-LSTM network extracts both space and time features of the received channel state information (CSI) from a single router. Furthermore, the proposed network builds a quantification model rather than a limited classification model as in most of the literature work, which enables the estimation of testing points that are not identical to the reference points. We analyze the instability of CSI and demonstrate a mitigation solution using a comprehensive filter and normalization scheme. The localization accuracy is investigated through extensive on-site experiments with several mobile devices including mobile phone (Nexus 5) and laptop (Intel 5300 NIC) on hundreds of testing locations. Using only a single WiFi router, our structure achieves an average localization error of 2.5 m with 80% of the errors under 4 m, which outperforms the other reported algorithms by approximately 50% under the same test environment.

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Applications of DSP

Cited by 7 publications

References 0 publications

Progression of Geographic Atrophy: Epistemic Uncertainties Affecting Mathematical Models and Machine Learning

Progression of Geographic Atrophy: Epistemic Uncertainties Affecting Mathematical Models and Machine Learning

Application of different control algorithms on a ‘home-made’ temperature control lab kit

A CNN-LSTM Quantifier for Single Access Point CSI Indoor Localization

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