An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

Khan, Junaid; Fayaz, Muhammad; Hussain, Ayyaz; Khalid, Shah; Khan, Wali Ullah; Gwak, Jeonghwan

doi:10.1109/access.2021.3073876

Cited by 19 publications

(14 citation statements)

References 47 publications

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“…Generally, when we have limited data and classes, the k-fold cross-validation can be applied to find better hyperparameters in practical applications [37]. However, it is not suitable for our study in terms of training time.…”

Section: B Preparation For Analysismentioning

confidence: 99%

TT-MLP: Tensor Train Decomposition on Deep MLPs

Yan

Ando

et al. 2023

IEEE Access

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Deep multilayer perceptrons (MLPs) have achieved promising performance on computer vision tasks. Deep MLPs consist solely of fully-connected layers as the conventional MLPs do but adopt more sophisticated network architectures based on mixer layers composed of token-mixing and channelmixing components. These architectures enable deep MLPs to have global receptive fields, but the significant increase of parameters becomes a massive burden on practical applications. To tackle this problem, we focus on using tensor-train decomposition (TTD) for compressing deep MLPs. At first, this paper analyzes deep MLPs under conventional TTD methods, especially using various designs of a macro framework and micro blocks: The former is how to concatenate mixer layers, and the latter is how to design a mixer layer. Based on the analysis, we propose a novel TTD method named Train-TTD-Train. The proposed method exerts the learning capability of channel-mixing components and improves the trade-off between accuracy and size. In the evaluation, the proposed method showed a better trade-off than conventional TTD methods on ImageNet-1K and achieved a 0.56% higher inference accuracy with a 15.44% memory reduction on Cifar-10.INDEX TERMS Tensor-train decomposition, low-rank approximation, deep neural networks, deep multilayer perceptron, network parameter compression.

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Section: B Preparation For Analysismentioning

confidence: 99%

TT-MLP: Tensor Train Decomposition on Deep MLPs

Yan

Ando

et al. 2023

IEEE Access

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“…However, the accuracy and performance of an algorithm are always challenging tasks for researchers to accurately predict under many circumstances such as multiple target tracking (MTT) [13] or in navigation systems [1]. Junaid et al [2] proposed a new approach to increase the performance of the alpha-beta filter algorithm. They applied a deep extreme learning machine algorithm for prediction accuracy, but the model had low performance and involved too much computation.…”

Section: Related Workmentioning

confidence: 99%

A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network

Khan

Lee

Kim

2022

CAAI Trans on Intel Tech

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The alpha–beta filter algorithm has been widely researched for various applications, for example, navigation and target tracking systems. To improve the dynamic performance of the alpha–beta filter algorithm, a new prediction learning model is proposed in this study. The proposed model has two main components: (1) the alpha–beta filter algorithm is the main prediction module, and (2) the learning module is a feedforward artificial neural network (FF‐ANN). Furthermore, the model uses two inputs, temperature sensor and humidity sensor data, and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings. Using the novel proposed technique, prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network, and also reduces the root mean square error (RMSE) and mean absolute error (MAE). We carried out different experiments with different experimental setups. The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter. A higher prediction accuracy was achieved, and the MAE and RMSE were 35.1%–38.2% respectively. The final proposed model results show increased performance when compared to traditional methods.

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“…Khan et al [30], introduced what they called the "new learning to the prediction model". The main idea was that once the filter parameters are established, they remain constant.…”

Section: The Alpha-beta Family Of Filtersmentioning

confidence: 99%

The Alpha-Beta Family of Filters to Solve the Threshold Problem: A Comparison

et al. 2022

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Typically, devices work to improve life quality, measure parameters, and make decisions. They also signalize statuses, and take actions accordingly. When working, they measure different values. These are to be compared against thresholds. Some time ago, vision systems came into play. They use camera(s) to deliver(s) images to a processor module. The received images are processed to perform detections (typically, they focus to detect objects, pedestrians, mopeds, cyclists, etc.). Images are analyzed and thresholds are used to compare the computed values. The important thing is that images are affected by noise. Therefore, the vision system performance can be affected by weather in some applications (for example, in automotive). An interesting case in this domain is when the measured/computed values show small variations near the threshold (not exceeding) but very close to it. The system is not able to signalize/declare a state in this case. It is also important to mention that changing the threshold does not guarantee solving the problem in any future case, since this may happen again. This paper proposes the Alpha-Beta family of filters as a solution to this problem. The members can track a signal based on measured values. This reveals errors when the tracked-signal’s first derivative changes sign. These errors are used in this paper to bypass the threshold problem. Since these errors appear in both situations (when the first derivative decreases from positive to negative and increases from negative to positive), the proposed method works when the observed data are in the vicinity of the threshold but above it.

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An Improved Alpha Beta Filter Using a Deep Extreme Learning Machine

Cited by 19 publications

References 47 publications

TT-MLP: Tensor Train Decomposition on Deep MLPs

TT-MLP: Tensor Train Decomposition on Deep MLPs

A higher prediction accuracy–based alpha–beta filter algorithm using the feedforward artificial neural network

The Alpha-Beta Family of Filters to Solve the Threshold Problem: A Comparison

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