An effective communication application necessitates the cancellation of Impulsive Noise(IN)from Orthogonal Frequency Division Multiplexing(OFDM),which is widely used for wireless applications due to its higher data ra...An effective communication application necessitates the cancellation of Impulsive Noise(IN)from Orthogonal Frequency Division Multiplexing(OFDM),which is widely used for wireless applications due to its higher data rate and greater spectral efficiency.The OFDM system is typically corrupted by Impulsive Noise,which is an unwanted short-duration pulse with random amplitude and duration.Impulsive noise is created by humans and has non-Gaussian characteristics,causing problems in communication systems such as high capacity loss and poor error rate performance.Several techniques have been introduced in the literature to solve this type of problem,but they still have many issues that affect the performance of the presented methods.As a result,developing a new hybridization-based method is critical for accurate method performance.In this paper,we present a hybrid of a state space adaptive filter and an information coding technique for cancelling impulsive noise from OFDM.The proposed method is also compared to Least Mean Square(LMS),Normalized Least Mean Square(NLMS),and Recursive Least Square(RLS)adaptive filters.It has also been tested using the binary phase-shift keyed(BPSK),four quadrature amplitude modulation(QAM),sixteen QAM,and thirty-two QAM modulation techniques.Bit error Rate(BER)simulations are used to evaluate system performance,and improved performance is obtained.Furthermore,the proposed method is more effective than recent methods.展开更多
Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substa...Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits.展开更多
基金This research was supported by the MSIT(Ministry of Science and ICT),Koreaunder the ICAN(ICT Challenge and Advanced Network of HRD)program(IITP-2022-2020-0-01832)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)the Soonchunhyang University Research Fund。
文摘An effective communication application necessitates the cancellation of Impulsive Noise(IN)from Orthogonal Frequency Division Multiplexing(OFDM),which is widely used for wireless applications due to its higher data rate and greater spectral efficiency.The OFDM system is typically corrupted by Impulsive Noise,which is an unwanted short-duration pulse with random amplitude and duration.Impulsive noise is created by humans and has non-Gaussian characteristics,causing problems in communication systems such as high capacity loss and poor error rate performance.Several techniques have been introduced in the literature to solve this type of problem,but they still have many issues that affect the performance of the presented methods.As a result,developing a new hybridization-based method is critical for accurate method performance.In this paper,we present a hybrid of a state space adaptive filter and an information coding technique for cancelling impulsive noise from OFDM.The proposed method is also compared to Least Mean Square(LMS),Normalized Least Mean Square(NLMS),and Recursive Least Square(RLS)adaptive filters.It has also been tested using the binary phase-shift keyed(BPSK),four quadrature amplitude modulation(QAM),sixteen QAM,and thirty-two QAM modulation techniques.Bit error Rate(BER)simulations are used to evaluate system performance,and improved performance is obtained.Furthermore,the proposed method is more effective than recent methods.
基金This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute(KHIDI),funded by the Ministry of Health&Welfare,Republic of Korea(grant number:HI21C1831)the Soonchunhyang University Research Fund.
文摘Human personality assessment using gait pattern recognition is one of the most recent and exciting research domains.Gait is a person’s identity that can reflect reliable information about his mood,emotions,and substantial personality traits under scrutiny.This research focuses on recognizing key personality traits,including neuroticism,extraversion,openness to experience,agreeableness,and conscientiousness,in line with the bigfive model of personality.We inferred personality traits based on the gait pattern recognition of individuals utilizing built-in smartphone sensors.For experimentation,we collected a novel dataset of 22 participants using an android application and further segmented it into six data chunks for a critical evaluation.After data pre-processing,we extracted selected features from each data segment and then applied four multiclass machine learning algorithms for training and classifying the dataset corresponding to the users’Big-Five Personality Traits Profiles(BFPT).Experimental results and performance evaluation of the classifiers revealed the efficacy of the proposed scheme for all big-five traits.