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Real-Time Zero-Phase Digital Filter Using Recurrent Neural Network
(2023-01-01)
Sinjanakhom, Tantep
;
Chivapreecha, Sorawat
This paper proposes a method to design and implement a zero-phase digital filter that can run in a real-time system. Generally, zero-phase filters are designed for non-causal systems only as the time-reversal operations are required. Thus, the typical usage of these filters is for offline applications. For this reason, we propose a real-time zero-phase digital filter that is designed based on a recurrent neural network model, particularly the gated recurrent units. The model learns to perform zero-phase filtering by using training data made from the filtered signals that are generated by using the conventionally designed zero-phase filter. The original digital filter used to create the dataset is an IIR filter performing forward-backward filtering. The best trained model yields the mean absolute loss values at approximately 0.001 and can process at least 30 times faster than real-time. Furthermore, the trained model was implemented as a 3-band zero-phase graphic equalizer to exhibit one of its applications.
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A comparative performance of real-time big data analytic architectures
(2019-07-01)
Sanla, Apisit
;
Numnonda, Thanisa
Nowadays, many organizations pay attention to the relevant technologies of Big Data to analyze more accurately, quickly, and efficiently. Real-time Big Data analytics is challenging due to the massive volume of complex data needed to distribute in processing. Therefore, in this research, we investigate two state-of-the-art architectures: Lambda and Kappa. The Kappa architecture is simply the Lambda architecture without the batch layer. To help businesses decide on the right architecture, their processing time, and resource utilization in the same environment need to be found out. Experiments had been carried out with the data size 3 MB, 30 MB, and 300 MB. The results showed that Lambda architecture outperforms Kappa architecture around 9% for the accuracy test when using processing time approximately 2.2 times more than Kappa architecture. Lambda architecture also used more 10-20% of CPU usage and 0.5 GB of RAM usage than Kappa architecture.
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Extended Extreme Learning Machine: A Novel Framework for Neural Network
(2018-07-02)
Jiaramaneepinit, Boonnithi
;
Nuthong, Chaiwat
Machine learning has been a focus research topic of superior tasks in many real-world applications. One of the famous preferred system is neural network. This approach has been invented for decades but becomes popular recently due to its satisfied results in many applications. The success of applying neural network involves model training which conventionally uses backpropagation method. However, it has many drawbacks. In recent decades, Extreme learning machine (ELM) was first proposed for training single-hidden layer feedforward neural network (SLFN). It optimizes training error by utilizing the whole training dataset with a one-shot calculation. However, for the training in datasets with large number of input features or high-dimensional datasets, original ELM encounters many difficulties. One of them is that the original ELM has no learning process from an input layer. This lead to an incomplete representation of data when it is transferred from one layer to another. Another difficulty involves training instability which causes fluctuation in testing accuracy. This is because networks' input weights are randomly generated. To circumvent these difficulties, the imposing architecture, namely Extended Extreme Learning Machine (X-ELM), is proposed. X-ELM uses ELM as an extension part in order to predict the outputs based on ensemble approach. The proposed framework extends the usage of ELM to apply to more complex network structures, such as networks with multiple hidden layers or networks with multiple computing systems. The proposed framework is applied to vehicles characteristic classifications' datasets. The experimental results show that X-ELM achieves better testing accuracy than of ELM in real-world applications.
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Real-Time Vision Based Human Height Measurement Using Sliding Window on Selected Candidates
(2018-07-02)
Dokthurian, Siriporn
;
Pluempitiwiriyawej, Charnchai
;
Wangsiripitak, Somkiat
This paper presents a real-time human height estimation using image sequences obtained from a single calibrated camera. For each image frame, the candidate value of human height is calculated based on the approximated 3D ground plane and 2D positions of human head top and foot bottom. Some candidates whose values do not differ much from the previous height estimate are selected; a sliding window is then applied on an array of those candidate height values; candidate heights bounded inside the sliding window that has the maximum votes and some candidate heights around that window are finally used in final height estimate. The proposed algorithm of sliding window on selected candidate helps achieve the average accuracy of height estimate at 99.05%; it is a 1.30% increase of accuracy when compared to the final height estimate from all candidates. The standard deviation also decreases from 4.26 to 1.58; the proposed method is superior in terms of accuracy and stability.
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