Search Results - (( features detection sensor algorithm ) OR ( based optimization based algorithm ))*
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1
Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors
Published 2025“…Optimization algorithms are successfully applied in the feature selection task in many systems. …”
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2
A reinforcement learning-based energy-efficient spectrum-aware clustering algorithm for cognitive radio wireless sensor network
Published 2016“…In this thesis, a Reinforcement Learning (RL) based clustering algorithm is proposed to address energy and Primary Users (PUs) detection challenges in CR-WSN. …”
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3
Novel chewing cycle approach for peak detection algorithm of chew count estimation
Published 2025“…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
Published 2025“…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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5
Development of control algorithm for a new 12s-6p single phase field excited flux switching motor
Published 2020“…For position detection, algorithms merely need a basic infrared transceiver sensor. …”
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6
Supervised deep learning algorithms for process fault detection and diagnosis under different temporal subsequence length of process data
Published 2025“…Current FDD technologies mostly rely on data-driven solutions by making full use of abundant process data collected by the state-of-the-art distributed process instruments and sensors. Deep learning algorithms were widely used among all the data-driven algorithms. …”
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Drowsiness Detection Using Ocular Indices from EEG Signal
Published 2022“…Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. …”
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3D LiDAR Vehicle Perception and Classification Using 3D Machine Learning Algorithm
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Wavelet based fault tolerant control of induction motor / Khalaf Salloum Gaeid
Published 2012“…The fault detection algorithm identifies the time and location of each fault. …”
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10
Advanced solar tracking system with temperature control and real-time monitoring
Published 2025“…These algorithms dynamically control the servo motor, relay module, and LCD display, adapting the system based on environmental conditions. …”
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Multi-sensor fusion based on multiple classifier systems for human activity identification
Published 2019“…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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Development of electromyography-controlled 3D printed robot hand and supervised machine learning for signal classification
Published 2019“…The electronic hardware and hand robot structure were integrated to develop an EMG controlled hand robot prototype, and its functionality was tested through three stages: muscular activity detection only, object detection only and the integration of both detection in an algorithm to control the hand robot structure movement to perform opened hand palm and some grasping postures with two trial for each stage. …”
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13
Square groove detection based on forstner with canny edge operator using laser vision sensor
Published 2023“…The objectives of 1his research are to develop a detection algorithm 1hat can extract 1he feature points of1he square-groove; and 1he second objective is to evaluate 1he detection algorithm and its ability to extract 1he image features of 1he square-groove in temis of 1he accuracy. …”
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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
Published 2019“…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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15
Data fusion and multiple classifier systems for human activity detection and health monitoring: Review and open research directions
Published 2019“…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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The noise reduction algorithm for star detection
Published 2025“…Accurate star detection plays a critical role in star sensors for spacecraft attitude determination. …”
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Indoor occupancy detection using machine learning and environmental sensors / Akindele Segun Afolabi ... [et al.]
Published 2025“…These algorithms were applied to data from environmental sensors such as temperature, humidity, carbon dioxide (CO2), and light sensors, and afterward. …”
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Landmark detection for vision based of autonomous guided vehicle
Published 2012“…Non-landmark might possibly have the same features or characteristic with landmarks. The landmarks must be detected and recognized by vision sensor accurately in a real-time. …”
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Undergraduates Project Papers -
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Performance comparison of classification algorithms for EEG-based remote epileptic seizure detection in wireless sensor networks
Published 2014“…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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Conference or Workshop Item -
20
Obstacle detection technique using multi sensor integration for small unmanned aerial vehicle
Published 2017“…In this paper, combination of both sensors based is proposed for a small UAV obstacle detection system. …”
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