Search Results - (( signal detection based algorithm ) OR ( based optimization means algorithm ))*

Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods by Mohd Sharif, Zakaria, Mohammad Fadhil, Abas, Fatimah, Dg Jamil, Norhafidzah, Mohd Saad, Addie, Irawan, Pebrianti, Dwi

“…The fitness function with the Genetic Algorithm (GA) optimization method is tested and evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and detection time. 51 sets of data have been collected using software in the loop (SITL) methods and are used to determine the effectiveness of the proposed fitness function and GA. …”
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Design and optimization of Levenberg-Marquardt based Neural Network Classifier for EMG signals to identify hand motions by Ibrahimy, Muhammad Ibn, Ahsan, Md. Rezwanul, Khalifa, Othman Omran

“…Different structures of neural network, based on the number of hidden neurons and two prominent training algorithms, have been considered in the research to find out their applicability for EMG signal classification. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa'

“…The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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Novel chewing cycle approach for peak detection algorithm of chew count estimation by Selamat, Nur Asmiza, Md Ali, Sawal Hamid, Ismail, Ahmad Ghadafi, Ahmad, Siti Anom, Minhad, Khairun Nisa’

“…The peak detection algorithm identifies key signal features, while PSO optimizes the peak prominence and width parameters to minimize the mean absolute error (MAE) in chew count estimation. …”
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Multichannel optimization with hybrid spectral- entropy markers for gender identification enhancement of emotional-based EEGs by Al-Qazzaz, Noor Kamal, Sabir, Mohannad K., Mohd Ali, Sawal Hamid, Ahmad, Siti Anom, Grammer, Karl

“…Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. …”
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Detecting problematic vibration on unmanned aerial vehicles via genetic-algorithm methods by Zakaria, Mohd Sharif, Abas, Mohammad Fadhil, Dg Jamil, Fatimah, Mohd Saad, Norhafidzah, Hashim, Addie Irawan, Pebrianti, Dwi

“…The fitness function with the Genetic Algorithm (GA) optimization method is tested and evaluated based on Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and detection time. 51 sets of data have been collected using software in the loop (SITL) methods and are used to determine the effectiveness of the proposed fitness function and GA. …”
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom, Sampe, Jahariah

“…In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. …”
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A novel peak detection algorithm using particle swarm optimization for chew count estimation of a contactless chewing detection by Selamat, Nur Asmiza, Md. Ali, Sawal Hamid, Minhad, Khairun Nisa’, Ahmad, Siti Anom

“…In accordance with the result, this article proposes a novel chew count estimation based on particle swarm optimization (PSO). First, the base of the algorithm is developed based on counting the peak of the chewing signal. …”
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Random subspace K-NN based ensemble classifier for driver fatigue detection utilizing selected EEG channels by Rashid, Mamunur, Mahfuzah, Mustafa, Norizam, Sulaiman, Nor Rul Hasma, Abdullah, Rosdiyana, Samad

“…In the present framework, a new channel selection algorithm based on correlation coefficients and an ensemble classifier based on random subspace k-nearest neighbour (k-NN) has been presented to enhance the classification performance of EEG data for driver fatigue detection. …”
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Development of an islanding detection scheme based on combination of slantlet transform and ridgelet probabilistic neural network in distributed generation by Ahmadipour, Masoud

“…The error measurements of the proposed method such as Mean Absolute Percentage Error, Mean Absolute Error, And Root Mean Square Error for islanding detection are less than 0.02% for ideal and noisy conditions which shows that the algorithm is not sensitive to noise. …”
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Dynamic transmit antenna shuffling scheme for hybrid multiple-input multiple-output in layered architecture by Chong, Jin Hui

“…The QR decomposition is a common signal processing technique for MIMO detection. The computational complexity (total number of arithmetic operations) of proposed LC-QR algorithm is significantly lower than the conventional QR decomposition, zero-forcing (ZF) and minimum mean square error (MMSE) detection algorithm. …”
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Breast cancer diagnosis through an optimization-driven multispectral gamma correction (ODMGC) by Raj A, Arul Edwin, Ahmad, Nabihah, Durai S, Ananiah

“…The ODMGC involves a multi-step optimisation process that categorises grey-scale images of breast thermograms based on mean brightness. Then, based on the grey levels of the pixels, we grouped each categorisation into sub-regions. …”
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Color image enhancement of acute leukemia cells in blood microscopic image for leukemia detection sample by Harun, Nor Hazlyna, Abu Bakar, Juhaida, Abd Wahab, Zulkifli, Osman, Muhammad Khusairi, Harun, Hazaruddin

“…The segmentation algorithm uses saturation S-component based on Hue, Saturation, Intensity (HSI) color model. …”
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A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure-free electroencephalography signals by Ong, Pauline, Zainuddin, Zarita, Kee, Huong Lai

“…In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. …”
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Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal by Adam, A., Ibrahim, Z., Mokhtar, N., Shapiai, M.I., Cumming, P., Mubin, M.

“…In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. …”
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A Novel PSO Based Fuzzy Controller for Robust Operation of Solid-State Transfer Switch and Fast Load Transfer in Power Systems by Sebastian G., Hannan M.A., Al-Shetwi A.Q., Ker P.J., Rahman M.S.A., Mansur M., Muttaqi K.M.

Optimization of coded signals based on wavelet neural network by Ahmed, Mustafa Sami

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A novel selection of optimal statistical features in the DWPT domain for discrimination of ictal and seizure‑free electroencephalography signals by Ong, Pauline, Zainuddin, Zarita, Kee, Huong Lai

“…In this present study, a novel feature selection scheme based on the discrete wavelet packet decomposition and cuckoo search algorithm (CSA) was proposed. …”
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Evaluation of Different Time Domain Peak Models using Extreme Learning Machine‐Based Peak Detection for EEG Signal by Asrul, Adam, Zuwairie, Ibrahim, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Cumming, Paul, Marizan, Mubin

“…In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. …”
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A Subspace Approach for Extracting Signals Highly Corrupted by Colored Noise by Yusoff, Mohd Zuki, Hussin, Fawnizu Azmadi

“…An optimization and eigen-decomposition-based subspace approach has been investigated and tested to estimate signals which are highly corrupted by colored noise; Hu and Loizou [Y. …”
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