Power Spectrum Density Values Research Articles

Implementing green optical waveform system using hybrid cognitive methods for QAM transmission scheme

Abstract This study proposes a hybrid approach combining Energy Detection (ED) and Matched Filter (MF) spectrum sensing techniques to enhance power spectrum density (PSD) in optical Nonorthogonal Multiple Access (NOMA) systems. Optical NOMA has emerged as a key technology for boosting spectral efficiency in optical communication networks. However, optimizing PSD remains a critical challenge due to various factors including signal detection and noise interference. The hybrid ED–MF spectrum sensing method aims to address these challenges by leveraging the strengths of both techniques. Energy Detection (ED) offers simplicity and robustness in detecting primary users, making it suitable for initial spectrum sensing in cognitive radio networks. Matched Filter (MF) spectrum sensing, on the other hand, provides superior signal detection and noise rejection capabilities, particularly in low signal-to-noise ratio (SNR) environments. By integrating these two techniques, we aim to achieve improved sensitivity and accuracy in spectrum sensing, thus enhancing spectral efficiency and system performance in optical NOMA networks. The effectiveness of the proposed hybrid approach is evaluated through theoretical analysis and simulation experiments. Results demonstrate significant enhancements in spectral efficiency and system reliability compared to conventional spectrum sensing methods, highlighting the potential of the hybrid ED–MF approach for enhancing PSD in optical NOMA systems. This research contributes to advancing the design and optimization of optical communication systems for future high-capacity and high-speed data transmission applications. The PSD values −920 are obtained and it confirmed that the proposed algorithm outperforms the standard algorithms.

Ensuring fair assessment of solid-state nanopore sensors with reporting baseline current

In developing solid-state nanopore sensors for single molecule detection, comprehensive evaluation of the nanopore quality is important. Existing studies typically rely on comparing the noise root mean square or power spectrum density values. Nanopores exhibiting lower noise values are generally considered superior. This evaluation is valid when the single molecule signal remains consistent. However, the signal can vary, as it is strongly related to the solid-state nanopore size, which is hard to control during fabrication consistently. This work emphasized the need to report the baseline current for evaluating solid-state nanopore sensors. The baseline current offers insight into several experimental conditions, particularly the nanopore size. Our experiments show that a nanopore sensor with more noise is not necessarily worse when considering the signal-to-noise ratio (SNR), particularly when the pore size is smaller. Our findings suggest that relying only on noise comparisons can lead to inaccurate evaluations of solid-state nanopore sensors, considering the inherent variability in fabrication and testing setups among labs and measurements. We propose that future studies should include reporting baseline current and sensing conditions. Additionally, using SNR as a primary evaluation tool for nanopore sensors could provide a more comprehensive understanding of their performance.

Neuroimaging Study of Brain Functional Differences in Generalized Anxiety Disorder and Depressive Disorder.

Generalized anxiety disorder (GAD) and depressive disorder (DD) are distinct mental disorders, which are characterized by complex and unique neuroelectrophysiological mechanisms in psychiatric neurosciences. The understanding of the brain functional differences between GAD and DD is crucial for the accurate diagnosis and clinical efficacy evaluation. The aim of this study was to reveal the differences in functional brain imaging between GAD and DD based on multidimensional electroencephalogram (EEG) characteristics. To this end, 10 min resting-state EEG signals were recorded from 38 GAD and 34 DD individuals. Multidimensional EEG features were subsequently extracted, which include power spectrum density (PSD), fuzzy entropy (FE), and phase lag index (PLI). Then, a direct statistical analysis (i.e., ANOVA) and three ensemble learning models (i.e., Random Forest (RF), Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost)) were used on these EEG features for the differential recognitions. Our results showed that DD has significantly higher PSD values in the alpha1 and beta band, and a higher FE in the beta band, in comparison with GAD, along with the aberrant functional connections in all four bands between GAD and DD. Moreover, machine learning analysis further revealed that the distinct features predominantly occurred in the beta band and functional connections. Here, we show that DD has higher power and more complex brain activity patterns in the beta band and reorganized brain functional network structures in all bands compared to GAD. In sum, these findings move towards the practical identification of brain functional differences between GAD and DD.

Whole-Body Vibrations Associated With Alpine Skiing: A Risk Factor for Low Back Pain?

Alpine skiing, both recreational and competitive, is associated with high rates of injury. Numerous studies have shown that occupational exposure to whole-body vibrations is strongly related to lower back pain and some suggest that, in particular, vibrations of lower frequencies could lead to overuse injuries of the back in connection with alpine ski racing. However, it is not yet known which forms of skiing involve stronger vibrations and whether these exceed safety thresholds set by existing standards and directives. Therefore, this study was designed to examine whole-body vibrations connected with different types of skiing and the associated potential risk of developing low back pain. Eight highly skilled ski instructors, all former competitive ski racers and equipped with five accelerometers and a Global Satellite Navigation System to measure vibrations and speed, respectively, performed six different forms of skiing: straight running, plowing, snow-plow swinging, basic swinging, short swinging, and carved turns. To estimate exposure to periodic, random and transient vibrations the power spectrum density (PSD) and standard ISO 2631-1:1997 parameters [i.e., the weighted root-mean-square acceleration (RMS), crest factor, maximum transient vibration value and the fourth-power vibration dose value (VDV)] were calculated. Ground reaction forces were estimated from data provided by accelerometers attached to the pelvis. The major novel findings were that all of the forms of skiing tested produced whole-body vibrations, with highest PSD values of 1.5–8 Hz. Intensified PSD between 8.5 and 35 Hz was observed only when skidding was involved. The RMS values for 10 min of short swinging or carved turns, as well as all 10-min equivalent VDV values exceeded the limits set by European Directive 2002/44/EC for health and safety. Thus, whole-body vibrations, particularly in connection with high ground reaction forces, contribute to a high risk for low back pain among active alpine skiers.

Cochlear implant features and listening effort induction: measurement of the mental workload experienced during a word in noise recognition task

Cochlear implant features and listening effort induction: measurement of the mental workload experienced during a word in noise recognition task

Two-branch wavelet denoising for accurate spectrum sensing in cognitive radios

In order to detect the unused spectrum bands (the spectrum holes) efficiently in cognitive radios with low signal-to-noise radio (SNR), we propose to adopt two independent branches of wavelet to detect the singularities of the received signals' power spectrum density (PSD). The sensing structure is flexible such that we can use one or two branches to cope with different SNRs. Under low SNR condition, each branch uses distinct characteristics between noise and signals in the wavelet transform to eliminate the singularities generated by the noise. By using bandpass filter to calculate PSD values of the subbands which are distinguished by the signal's singularities, the subband with the minimum PSD value among all of the subbands could be found. Then, the results of the two branches are merged and analyzed in order to make the final decision. Finally, we use signal reconstruction to further remove the noise and then accurately detect the spectrum holes. When the SNR is high, only one branch through the denoising procedure is needed to get accurate sensing result. Our simulation results show that the two-branch wavelet method is more accurate than conventional approaches under given SNRs.