Advanced Mobile Communication Techniques in the Fight against the COVID-19 Pandemic Era and Beyond: An Overview of 5G/B5G/6G.

The coronavirus disease 2019 (COVID-19) pandemic has severely affected people's lives worldwide in an unexpected manner. According to the World Health Organization (WHO), several viral epidemics continue to occur and pose a significant public health problem. Until May 2023, there have been 676 million cases of COVID-19 infections and over 6.8 million deaths, globally. This paper surveys the role and effectiveness of advanced fifth-generation (5G)/beyond 5G (B5G)/sixth-generation (6G) technologies, combined with mobile applications (apps) and the Internet of Medical Things (IoMT), in detecting, managing, and mitigating the spread of COVID-19 and designing smart healthcare infrastructures for future pandemics. Analyzing and summarizing the research of relevant scholars based on the impact of 5G/B5G/6G and other technologies on COVID-19. The study tabulates the technical characteristics and effectiveness of different technologies in the context of COVID-19, summarizing the research of previous scholars. Challenges and design issues in the implementation of advanced information and telecommunication systems were demonstrated. These technologies can inspire the design of smart healthcare infrastructures to combat future virus pandemics.

New Marginal Spectrum Feature Information Views of Humpback Whale Vocalization Signals Using the EMD Analysis Methods.

Marginal spectrum (MS) feature information of humpback whale vocalization (HWV) signals is an interesting and significant research topic. Empirical mode decomposition (EMD) is a powerful time-frequency analysis tool for marine mammal vocalizations. In this paper, new MS feature innovation information of HWV signals was extracted using the EMD analysis method. Thirty-six HWV samples with a time duration of 17.2 ms were classified into Classes I, II, and III, which consisted of 15, 5, and 16 samples, respectively. The following ratios were evaluated: the average energy ratios of the 1 first intrinsic mode function (IMF1) and residual function (RF) to the referred total energy for the Class I samples; the average energy ratios of the IMF1, 2nd IMF (IMF2), and RF to the referred total energy for the Class II samples; the average energy ratios of the IMF1, 6th IMF (IMF6), and RF to the referred total energy for the Class III samples. These average energy ratios were all more than 10%. The average energy ratios of IMF1 to the referred total energy were 9.825%, 13.790%, 4.938%, 3.977%, and 3.32% in the 2980-3725, 3725-4470, 4470-5215, 10,430-11,175, and 11,175-11,920 Hz bands, respectively, in the Class I samples; 14.675% and 4.910% in the 745-1490 and 1490-2235 Hz bands, respectively, in the Class II samples; 12.0640%, 6.8850%, and 4.1040% in the 2980-3725, 3725-4470, and 11,175-11,920 Hz bands, respectively, in the Class III samples. The results of this study provide a better understanding, high resolution, and new innovative views on the information obtained from the MS features of the HWV signals.

EMD-Based Energy Spectrum Entropy Distribution Signal Detection Methods for Marine Mammal Vocalizations.

To develop a passive acoustic monitoring system for diversity detection and thereby adapt to the challenges of a complex marine environment, this study harnesses the advantages of empirical mode decomposition in analyzing nonstationary signals and introduces energy characteristics analysis and entropy of information theory to detect marine mammal vocalizations. The proposed detection algorithm has five main steps: sampling, energy characteristics analysis, marginal frequency distribution, feature extraction, and detection, which involve four signal feature extraction and analysis algorithms: energy ratio distribution (ERD), energy spectrum distribution (ESD), energy spectrum entropy distribution (ESED), and concentrated energy spectrum entropy distribution (CESED). In an experiment on 500 sampled signals (blue whale vocalizations), in the competent intrinsic mode function (IMF2) signal feature extraction function distribution of ERD, ESD, ESED, and CESED, the areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were 0.4621, 0.6162, 0.3894, and 0.8979, respectively; the Accuracy scores were 49.90%, 60.40%, 47.50%, and 80.84%, respectively; the Precision scores were 31.19%, 44.89%, 29.44%, and 68.20%, respectively; the Recall scores were 42.83%, 57.71%, 36.00%, and 84.57%, respectively; and the F1 scores were 37.41%, 50.50%, 32.39%, and 75.51%, respectively, based on the threshold of the optimal estimated results. It is clear that the CESED detector outperforms the other three detectors in signal detection and achieves efficient sound detection of marine mammals.

IMF-Based MF and HS Energy Feature Information of F5, and F6 Movement and Motor Imagery EEG Signals in Delta Rhythms Using HHT.

This study aims to extract the energy feature distributions in the form of marginal frequency (MF) and Hilbert spectrum (HS) in the intrinsic mode functions (IMF) domain for actual movement (AM)-based and motor imagery (MI)-based electroencephalogram (EEG) signals using the Hilbert-Huang transformation (HHT) time frequency (TF) analysis method. Accordingly, F5 and F6 EEG signal TF energy feature distributions in delta (0.5-4 Hz) rhythm are explored. We propose IMF-based and residue function (RF)-based MF and HS feature information extraction methods with IMFRFERDD (IMFRF energy refereed distribution density), IMFRFMFERDD (IMFRF MF energy refereed distribution density), and IMFRFHSERDD (IMFRF HS energy refereed distribution density) parameters using HHT with application to AM, MI EEG F5, and F6 signals in delta rhythm. The AM and MI tasks involve simultaneously opening fists and feet, as well as simultaneously closing fists and feet. Eight samples (32 in total) with a time duration of 1000 ms are extracted for analyzing F5AM, F5MI, F6AM, and F6MI EEG signals, which are decomposed into five IMFs and one RF. The maximum average IMFRFERDD values of IMF4 are 3.70, 3.43, 3.65, and 3.69 for F5AM, F5MI, F6 AM, and F6MI, respectively. The maximum average IMFRFMFERDD values of IMF4 in the delta rhythm are 21.50, 20.15, 21.02, and 17.30, for F5AM, F5MI, F6AM, and F6MI, respectively. Additionally, the maximum average IMFRFHSERDD values of IMF4 in delta rhythm are 39,21, 39.14, 36.29, and 33.06 with time intervals of 500-600, 800-900, 800-900, and 500-600 ms, for F5AM, F5MI, F6AM, and F6MI, respectively. The results of this study, advance our understanding of meaningful feature information of F5MM, F5MI, F6MM, and F6MI, enabling the design of MI-based brain-computer interface assistive devices for disabled persons.

Extraction of Energy Characteristics of Blue Whale Vocalizations Based on Empirical Mode Decomposition.

This study extracts the energy characteristic distributions of the intrinsic mode functions (IMFs) and residue functions (RF) for a blue whale sound signal, with empirical mode decomposition (EMD) as the basic theoretical framework. A high-resolution marginal frequency characteristics extraction method, based on EMD with energy density intensity (EDI) parameters for blue B call vocalizations, was proposed. The extraction algorithm included six steps: EMD, energy analysis, marginal frequency (MF) analysis with EDI parameters, feature extraction (FE), classification, and Hilbert spectrum (HS) analysis. The blue whale sound sources were obtained from the website of the Scripps Whale Acoustics Lab of the University of California, San Diego, USA. The source is a type of B call with a time duration of 46.65 s, from which 59 analysis samples with a time duration of 180 ms were taken. The average energy distribution ratios of the IMF1, IMF2, IMF3, IMF4, and RF are 49.06%, 20.58%, 13.51%, 10.94% and 3.84%, respectively. New classification criteria and EDI parameters were proposed to extract the blue whale B call vocalization (BWBCV) characteristics. The analysis results show that the main frequency bands of the signal are distributed at 41-43 Hz in the MF of IMF1 for Class I BWBCV and 11-13 Hz in the MF of IMF2 for Class II BWBCV, respectively.

Generalized Frequency Division Multiplexing-Based Low-Power Underwater Acoustic Image Transceiver.

In this paper, a low-power underwater acoustic (UWA) image transceiver based on generalized frequency division multiplexing (GFDM) modulation for underwater communication is proposed. The proposed transceiver integrates a low-density parity-check code error protection scheme, adaptive 4-quadrature amplitude modulation (QAM) and 16-QAM strategies, GFDM modulation, and a power assignment mechanism in an UWA image communication environment. The transmission bit error rates (BERs), the peak signal-to-noise ratios (PSNRs) of the received underwater images, and the power-saving ratio (PSR) of the proposed transceiver obtained using 4-QAM and 16-QAM, with perfect channel estimation, and channel estimation errors (CEEs) of 5%, 10%, and 20% were simulated. The PSNR of the received underwater image is 44.46 dB when using 4-QAM with a CEE of 10%. In contrast, PSNR is 48.79 dB when using 16-QAM with a CEE of 10%. When BER is 10-4, the received UW images have high PSNR values and high resolutions, indicating that the proposed transceiver is suitable for underwater image sensor signal transmission.

The energy based characteristics of sperm whale clicks using the Hilbert Huang transform analysis method.

In this paper, a unique analysis method for sperm whale clicks based on Hilbert-Huang transform (HHT) is proposed. Four sperm whale click samples with durations of 10 ms (defined as click I), and four sperm whale click samples with durations of 5 ms (defined as click II) were illustrated. These click samples were recorded in the Mediterranean Sea by Centro Interdisciplinare di Bioacusticae Ricerche Ambientali, Università degli Studi di Pavia. The empirical mode decomposition method was used to decompose click I samples into seven intrinsic mode functions (IMFs) and one residue function (RF), and click II samples were decomposed into six IMFs and one RF. The average energy distributions of multiple IMFs and the single RF domain for click I and click II samples were explored using the HHT analysis method. The average energy-frequency representations were also investigated for the same click I and click II samples. The analysis results show that the energy-frequency characteristics of sperm whale clicks can be extracted and understood by applying several IMFs and one RF signal with a high-resolution analysis.

Chaotic Visual Cryptosystem Using Empirical Mode Decomposition Algorithm for Clinical EEG Signals.

This paper, proposes a chaotic visual cryptosystem using an empirical mode decomposition (EMD) algorithm for clinical electroencephalography (EEG) signals. The basic design concept is to integrate two-dimensional (2D) chaos-based encryption scramblers, the EMD algorithm, and a 2D block interleaver method to achieve a robust and unpredictable visual encryption mechanism. Energy-intrinsic mode function (IMF) distribution features of the clinical EEG signal are developed for chaotic encryption parameters. The maximum and second maximum energy ratios of the IMFs of a clinical EEG signal to its refereed total energy are used for the starting points of chaotic logistic map types of encrypted chaotic signals in the x and y vectors, respectively. The minimum and second minimum energy ratios of the IMFs of a clinical EEG signal to its refereed total energy are used for the security level parameters of chaotic logistic map types of encrypted chaotic signals in the x and y vectors, respectively. Three EEG database, and seventeen clinical EEG signals were tested, and the average r and mse values are 0.0201 and 4.2626 × 10(-29), respectively, for the original and chaotically-encrypted through EMD clinical EEG signals. The chaotically-encrypted signal cannot be recovered if there is an error in the input parameters, for example, an initial point error of 0.000001 %. The encryption effects of the proposed chaotic EMD visual encryption mechanism are excellent.

Hilbert-Huang Transformation Based Analyses of FP1, FP2, and Fz Electroencephalogram Signals in Alcoholism.

Chronic alcoholism may damage the central nervous system, causing imbalance in the excitation-inhibition homeostasis in the cortex, which may lead to hyper-arousal of the central nervous system, and impairments in cognitive function. In this paper, we use the Hilbert-Huang transformation (HHT) method to analyze the electroencephalogram (EEG) signals from control and alcoholic observers who watched two different pictures. We examined the intrinsic mode function (IMF) based energy distribution features of FP1, FP2, and Fz EEG signals in the time and frequency domains for alcoholics. The HHT-based characteristics of the IMFs, the instantaneous frequencies, and the time-frequency-energy distributions of the IMFs of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers who watched two different pictures were analyzed. We observed that the number of peak amplitudes of the alcoholic subjects is larger than that of the control. In addition, the Pearson correlation coefficients of the IMFs, and the energy-IMF distributions of the clinical FP1, FP2, and Fz EEG signals recorded from normal and alcoholic observers were analyzed. The analysis results show that the energy ratios of IMF4, IMF5, and IMF7 waves of the normal observers to the refereed total energy were larger than 10 %, respectively. In addition, the energy ratios of IMF3, IMF4, and IMF5 waves of the alcoholic observers to the refereed total energy were larger than 10 %. The FP1 and FP2 waves of the normal observers, the FP1 and FP2 waves of the alcoholic observers, and the FP1 and Fz waves of the alcoholic observers demonstrated extremely high correlations. On the other hand, the FP1 waves of the normal and alcoholic observers, the FP1 wave of the normal observer and the FP2 wave of the alcoholic observer, the FP1 wave of the normal observer and the Fz wave of the alcoholic observer, the FP2 waves of the normal and alcoholic FP2 observers, and the FP2 wave of the normal observer and the Fz wave of the alcoholic observer demonstrated extremely low correlations. The IMF4 of the FP1 and FP2 signals of the normal observer, and the IMF5 of the FP1 and FP2 signals of the alcoholic observer were correlated. The IMF4 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer as well as the IMF5 of the FP1 signal of the normal observer and that of the FP2 signal of the alcoholic observer exhibited extremely low correlations. In this manner, our experiment leads to a better understanding of the HHT-based IMFs features of FP1, FP2, and Fz EEG signals in alcoholism. The analysis results show that the energy ratios of the wave of an alcoholic observer to its refereed total energy for IMF4, and IMF5 in the δ band for FP1, FP2, and Fz channels were larger than those of the respective waves of the normal observer. The alcoholic EEG signals constitute more than 1 % of the total energy in the δ wave, and the reaction times were 0_4, 4_8, 8_12, and 12_16 s. For normal EEG signals, more than 1 % of the total energy is distributed in the δ wave, with a reaction time 0 to 4 s. We observed that the alcoholic subject reaction times were slower than those of the normal subjects, and the alcoholic subjects could have experienced a cognitive error. This phenomenon is due to the intoxicated central nervous systems of the alcoholic subjects.

Analysis of spike waves in epilepsy using Hilbert-Huang transform.

In this paper, we used the Hilbert-Huang transform (HHT) analysis method to examine the time-frequency characteristics of spike waves for detecting epilepsy symptoms. We obtained a sample of spike waves and nonspike waves for HHT decomposition by using numerous intrinsic mode functions (IMFs) of the Hilbert transform (HT) to determine the instantaneous, marginal, and Hilbert energy spectra. The Pearson correlation coefficients of the IMFs, and energy-IMF distributions for the electroencephalogram (EEG) signal without spike waves, Spike I, Spike II and Spike III sample waves were determined. The analysis results showed that the ratios of the referred wave and Spike III wave to the referred total energy for IMF1, IMF2, and the residual function exceeded 10%. Furthermore, the energy ratios for IMF1, IMF2, IMF3 and the residual function of Spike I, Spike II to their total energy exceeded 10%. The Pearson correlation coefficients of the IMF3 of the EEG signal without spike waves and Spike I wave, EEG signal without spike waves and Spike II wave, EEG signal without spike waves and Spike III wave, Spike I and II waves, Spike I and III waves, and Spike II and III waves were 0.002, 0.06, 0.01, 0.17, 0.03, and 0.3, respectively. The energy ratios of IMF3 in the δ band to its referred total energy for the EEG signal without spike waves, and of the Spike I, II, and III waves were 4.72, 6.75, 5.41, and 5.55%, respectively. The weighted average frequency of the IMF1, IMF2, and IMF3 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of the spike waves, respectively. The weighted average magnitude of the IMF3, IMF4, and IMF5 of the EEG signal without spike waves was lower than that of the IMF1, IMF2, and IMF3 of spike waves, respectively.

Chaos based encryption system for encrypting electroencephalogram signals.

In the paper, we use the Microsoft Visual Studio Development Kit and C# programming language to implement a chaos-based electroencephalogram (EEG) encryption system involving three encryption levels. A chaos logic map, initial value, and bifurcation parameter for the map were used to generate Level I chaos-based EEG encryption bit streams. Two encryption-level parameters were added to these elements to generate Level II chaos-based EEG encryption bit streams. An additional chaotic map and chaotic address index assignment process was used to implement the Level III chaos-based EEG encryption system. Eight 16-channel EEG Vue signals were tested using the encryption system. The encryption was the most rapid and robust in the Level III system. The test yielded superior encryption results, and when the correct deciphering parameter was applied, the EEG signals were completely recovered. However, an input parameter error (e.g., a 0.00001 % initial point error) causes chaotic encryption bit streams, preventing the recovery of 16-channel EEG Vue signals.

Hilbert-Huang transformation-based time-frequency analysis methods in biomedical signal applications.

Hilbert-Huang transformation, wavelet transformation, and Fourier transformation are the principal time-frequency analysis methods. These transformations can be used to discuss the frequency characteristics of linear and stationary signals, the time-frequency features of linear and non-stationary signals, the time-frequency features of non-linear and non-stationary signals, respectively. The Hilbert-Huang transformation is a combination of empirical mode decomposition and Hilbert spectral analysis. The empirical mode decomposition uses the characteristics of signals to adaptively decompose them to several intrinsic mode functions. Hilbert transforms are then used to transform the intrinsic mode functions into instantaneous frequencies, to obtain the signal's time-frequency-energy distributions and features. Hilbert-Huang transformation-based time-frequency analysis can be applied to natural physical signals such as earthquake waves, winds, ocean acoustic signals, mechanical diagnosis signals, and biomedical signals. In previous studies, we examined Hilbert-Huang transformation-based time-frequency analysis of the electroencephalogram FPI signals of clinical alcoholics, and 'sharp I' wave-based Hilbert-Huang transformation time-frequency features. In this paper, we discuss the application of Hilbert-Huang transformation-based time-frequency analysis to biomedical signals, such as electroencephalogram, electrocardiogram signals, electrogastrogram recordings, and speech signals.

Mobile telemedicine: a survey study.

Telemedicine involves the use of advanced and reliable communication techniques to deliver biomedical signals over long distances. In such systems, biomedical information is transmitted using wireline or wireless communication systems. Mobile telemedicine is an improved form of telemedicine, in which advanced wireless communication systems are used to deliver the biomedical signals of patients at any place and any time. Mobile telemedicine employs advanced concepts and techniques from the fields of electrical engineering, computer science, biomedical engineering, and medicine to overcome the restrictions involved in conventional telemedicine and realize an improvement in the quality of service of medicine. In this paper, we study several mobile telemedicine systems, and it is important to gain a good understanding of mobile telemedicine systems because in the further, such systems are expected to become ubiquitous for the delivery of biomedical signals for medicine.

Systematic behavior research for understanding consumer decision making.

This study incorporates means-end chain (MEC) theory and dynamic programming for understanding the implications of consumer decision making. The conceptual framework of this study can help programmers design information systems for analyzing consumption behaviors. Such analyses will provide marketers with meaningful information for formulating marketing strategies. The main contributions of this article are as follows: (1) to enable researchers to obtain information for consumer cognitive hierarchies utilizing an information system, (2) to enhance the functions of traditional MEC methodology and provide an integrated method for analyzing consumption information, and (3) to construct an information system for analyzing consumer decision-making processes.

A chaos-based visual encryption mechanism for clinical EEG signals.

In this study, we have developed a chaos-based visual encryption mechanism that can be applied for clinical electroencephalography (EEG) signals. In comparison with other types of random sequences, chaos sequences were mainly used to increase unpredictability. We used a 1D chaotic scrambler and a permutation scheme to achieve EEG visual encryption. One approach of realizing the visual encryption mechanism is to scramble the signal values of the input EEG signal by multiplying a 1D chaotic signal to randomize the EEG signal values. We then applied a chaotic address scanning order encryption to the randomized reference values. Simulation results show that when the correct deciphering parameters are entered, the signal is completely recovered, and the percent root-mean-square difference (PRD) values for control and alcoholic clinical EEG signals are 4.33 x 10(-15) and 4.11 x 10(-15)%, respectively. As long as there is an input parameter error, with an initial point error of 0.00000001% as an example, thereby making these clinical EEG signals unrecoverable.

A decision-making process model of young online shoppers.

Based on the concepts of brand equity, means-end chain, and Web site trust, this study proposes a novel model called the consumption decision-making process of adolescents (CDMPA) to understand adolescents' Internet consumption habits and behavioral intention toward particular sporting goods. The findings of the CDMPA model can help marketers understand adolescents' consumption preferences and habits for developing effective Internet marketing strategies.

The cyber-aspects of virtual communities: free downloader ethics, cognition, and perceived service quality.

This study examined the downloader cognitive structures toward Web service quality and the downloader ethical attitudes across various levels of participation in a virtual community. Using four types of free downloads as the research subjects, the researcher found that the users in different participation degrees have different perception preferences. Owners of the free downloading Web sites can use the findings of this study to develop effective Web marketing strategies.

Predictors of betel quid chewing behavior and cessation patterns in Taiwan aborigines.

BACKGROUND: Betel quid, chewed by about 600 million people worldwide, is one of the most widely used addictive substances. Cessation factors in betel quid chewers are unknown. The present study explores prevalence and the quit rate of betel quid chewing in Taiwan aborigines. Our goal was to delineate potential predictors of chewing cessation. METHODS: A stratified random community-based survey was designed for the entire aborigines communities in Taiwan. A total of 7144 participants were included between June 2003 and May 2004 in this study. Information on sociodemographic characteristics, such as gender, age, obesity, education years, marital status, ethnicity, and habits of betel quid chewing, smoking and drinking was collected by trained interviewers. RESULTS: The prevalence of betel quid chewers was 46.1%. Betel quid chewing was closely associated with obesity (OR = 1.61; 95% CI: 1.40-1.85). Betel quid chewers were most likely to use alcohol and cigarettes together. Quit rate of betel quid chewers was 7.6%. Betel quid chewers who did not drink alcohol were more likely to quit (OR = 1.89; 95% CI: 1.43-2.50). Alcohol use is a significant factor related to cessation of betel quid chewing, but smoking is not. CONCLUSION: Taiwan aborigines have a high prevalence of betel quid chewers and a low quit rate. Alcohol use is strongly association with betel quid chewing. Efforts to reduce habitual alcohol consumption might be of benefit in cessation of betel quid chewing.

Downlink power control in multi-code CDMA for mobile medicine.

In this paper, we propose a downlink power-control mechanism to be applied in a multi-code code division multiple access (CDMA) mobile medicine system. The mobile medicine system can provide (i) measured blood pressure and body temperature, (ii) medical signals measured by the electrocardiogram (ECG) device, (iii) mobile patient's history, (iv) G.729 audio signals, (v) Joint Photographic Experts Group 2000 Medical images and Moving Picture Experts Group 4 charge-coupled device sensor video signals. By utilizing a multi-code CDMA spread spectrum communication system with downlink power-control strategy, it is possible for this system to meet the quality of service requirements of a mobile medicine network. In addition, such a system can maximize the resource utilization. For different messages to be sent, the power is controlled according to the requisite bit error rate (BER). Higher transmission power is given to the media with lower BER requirement. Numerical analysis shows that the ratios of transmission power for voice, video, and data virtual channels is approximately 1:2:13 when the BERs for voice, video, and data are 10(wedge)(- 3), 10(wedge)(- 4), and 10(wedge)(- 7), respectively. This power ratio is similar to the ratio of signal-to-noise plus interference power ratio for voice, video, and data during transmission. For the purpose of verifying the proposed argument, a simulation has been done and the results match the derivation very well.