Search Results - (( parallel estimation method algorithm ) OR ( features extraction sensor algorithm ))

Simulated kalman filter (SKF) based image template matching for distance measurement by using stereo vision system by Nurnajmin Qasrina Ann, Ayop Azmi

“…Stereo vision sensor can provide color and texture information for easy data and feature extraction. …”
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Square groove detection based on forstner with canny edge operator using laser vision sensor by Mohammed Naji, Osamah Abdullah Ahmed

“…This research addresses 1he feature extraction algorithm of 1he square weld groove to make weld seam recognition more accurate. …”
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Feature extraction and supervised learning for volatile organic compounds gas recognition by Mohd Tombel, Nor Syahira, Mohd Zaki, Hasan Firdaus, Mohd Fadglullah, Hanna Farihin

“…This research project aims to investigate effective feature extraction techniques that can be employed as discriminative features for machine learning algorithms. …”
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Wearable Sensor Feature Fusion for Human Activity Recognition (HAR) : A Proposed Classification Framework by Norfadzlan, Yusup, Adnan Shahid, Khan, Izzatul Nabila, Sarbini, Nurul Zawiyah, Mohamad

“…Due to the extensive feature engineering and human feature extraction required by traditional machine learning algorithms, they are time consuming to develop. …”
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Multi-sensor fusion based on multiple classifier systems for human activity identification by Nweke, Henry Friday, Teh, Ying Wah, Mujtaba, Ghulam, Alo, Uzoma Rita, Al-garadi, Mohammed Ali

“…To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. …”
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Feature extraction from oil palm in vitro shoot images by Ismail, Abdul Halim, Marhaban, Mohammad Hamiruce, Mohd Noor, Samsul Bahari, Hashim, Ahmad Tarmizi

“…The feature extraction techniques are discussed in this article. …”
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Internet of Things (IoT) based activity recognition strategies in smart homes: a review by Babangida, Lawal, Perumal, Thinagaran, Mustapha, Norwati, Yaakob, Razali

“…This technique is challenged by the nature of IoT technology and perceived data, as well as by human differences, which necessitated additional processing tasks to select significant features for the learning algorithms. In this work, we focus our review on activity recognition implementation strategies by examining various sensors and sensing technologies used to collect useful data from IoT devices, reviewing preprocessing and feature extraction techniques, as well as classification algorithms used to recognize human activities in smart homes. …”
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Parallel visualization approach of a 3D heart model by Malekpour, A., Ling, T.C., Lim, W.C.

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Multi-sensor fusion and deep learning framework for automatic human activity detection and health monitoring using motion sensor data / Henry Friday Nweke by Henry Friday , Nweke

“…First, to investigate existing multi-sensor and automatic feature extraction methods for human activity detection and health monitoring using motion sensor. …”
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The noise reduction algorithm for star detection by Razaman, Anis Hannani, Ahmad, Yasser Asrul, Gunawan, Teddy Surya, Khalifa, Othman Omran, Saidin, Norazlina

“…This improvement in denoising leads to more precise centroid extraction, contributing to more reliable star sensor performance for space navigation applications…”
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Designing an expressive virtual Kompang on mobile device with tri-axial accelerometer by Leng, Hoo Yong, Mohd Norowi, Noris, Jantan, Azrul Hazri

“…This paper is therefore implemented a simple extraction method by extracting percussive features from embedded sensors to map with the output sound with minimum delay. …”
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Evaluations of oil palm fresh fruit bunches maturity degree using multiband spectrometer by Tuerxun, Adilijiang

“…The result illustrated that just one sensor features (sensor 4) are significantly enough to build a good, accurate classifier model that can predict, and classify the oil palm FFB ripeness, rather than the proposed 4 bands sensors with 32 feature attributes. …”
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Sensors Fusion based Online Mapping and Features Extraction of Mobile Robot in the Road Following and Roundabout by Ali, Mohammed A. H., Musa, Mailah, Wan Azhar, Wan Yusoff, Zamzuri, Hamedon, Zulkifli, Md. Yusof, Anwar, P. P. Abdul Majeed

“…A road feature extraction based mapping system using a sensor fusion technique for mobile robot navigation in road environments is presented in this paper. …”
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Class binarization with self-adaptive algorithm to improve human activity recognition by Zainudin, Muhammad Noorazlan Shah

“…Somehow, it might occur some of extracted features are insignificant to describe the activity. …”
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Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions by Nweke, Henry Friday, Teh, Ying Wah, Mujtaba, Ghulam, Al-Garadi, Mohammed Ali

“…These sensors are pre-processed and different feature sets such as time domain, frequency domain, wavelet transform are extracted and transform using machine learning algorithm for human activity classification and monitoring. …”
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Digital quadrature compensators scheme for analog imperfections of quadrature modulator in wireless communication systems by Talebpour, Faraz

“…Offline on the other hand, is a mode where adaptive algorithms cannot estimate the imperfections in parallel with the transmission. …”
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Comparison Of Ridge Filters Performance in Human Vein Extraction Using 850nm Near Infrared Led Illumination by Abu Bakar Abd Rahman, Abdullah Bade, Fuei, Pien Chee

“…This paper investigates the performance of the ridge filters algorithm for vein feature extraction by using the NIR imaging technique to visualise human vein vessels. …”
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Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks by Abualsaud, Khalid, Mahmuddin, Massudi, Saleh, Mohammad, Mohamed, Amr

“…Identification of epileptic seizure remotely by analyzing the electroencephalography (EEG) signal is very important for scalable sensor-based health systems.Classification is the most important technique for wide-ranging applications to categorize the items according to its features with respect to predefined set of classes.In this paper, we conduct a performance evaluation based on the noiseless and noisy EEG-based epileptic seizure data using various classification algorithms including BayesNet, DecisionTable, IBK, J48/C4.5, and VFI.The reconstructed and noisy EEG data are decomposed with discrete cosine transform into several sub-bands.In addition, some of statistical features are extracted from the wavelet coefficients to represent the whole EEG data inputs into the classifiers.Benchmark on widely used dataset is utilized for automatic epileptic seizure detection including both normal and epileptic EEG datasets.The classification accuracy results confirm that the selected classifiers have greater potentiality to identify the noisy epileptic disorders.…”
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Bayesian Network Classifiers for Damage Detection in Engineering Material by Mohamed Addin, Addin Osman

“…The methodology used in the thesis to implement the Bayesian network for the damage detection provides a preliminary analysis used in proposing a novel fea- ture extraction algorithm (f-FFE: the f-folds feature extraction algorithm). …”
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Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud by Mohd Romlay, Muhammad Rabani, Mohd Ibrahim, Azhar, Toha, Siti Fauziah, De Wilde, Philippe, Venkat, Ibrahim

“…However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. …”
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