Noise-Loop Multiple Access for wireless communications

PurposePenetration state is one of the most important factors for judging the quality of a gas tungsten arc welding (GTAW) joint. The purpose of this paper is to identify and classify the penetration state and welding quality through the features of arc sound signal during robotic GTAW process.Design/methodology/approachThis paper tried to make a foundation work to achieve on‐line monitoring of penetration state to weld pool through arc sound signal. The statistic features of arc sound under different penetration states like partial penetration, full penetration and excessive penetration were extracted and analysed, and wavelet packet analysis was used to extract frequency energy at different frequency bands. The prediction models were established by artificial neural networks based on different features combination.FindingsThe experiment results demonstrated that each feature in time and frequency domain could react the penetration behaviour, arc sound in different frequency band had different performance at different penetration states and the prediction model established by 23 features in time domain and frequency domain got the best prediction effect to recognize different penetration states and welding quality through arc sound signal.Originality/valueThis paper tried to make a foundation work to achieve identifying penetration state and welding quality through the features of arc sound signal during robotic GTAW process. A total of 23 features in time domain and frequency domain were extracted at different penetration states. And energy at different frequency bands was proved to be an effective factor for identifying different penetration states. Finally, a prediction model built by 23 features was proved to have the best prediction effect of welding quality.

Third order time and frequency analysis has exhibited great potential for correlation analysis of multi-sensor datasets, but is usually presented as separate time domain and frequency domain approaches. A combined framework of both frequency domain and time domain has rarely been used. This paper proposes a non-parametric third order time and frequency domain framework which used two dimensional Fourier transforms to bridge the gap between time domain and frequency domain. A unified framework offers flexibility and efficiency to apply to data. In this paper we study neural spike train data treated as stochastic point processes. In time domain direct analysis, third order cumulant densities of spike trains are applied, which need all first-, secondand third order product densities to be calculated before constructing the third order cumulant density, which brings additional challenges. The novelty in this study is that a new framework is proposed which can offer an alternative approach without calculating lower order quantities and can reveal nonlinear relationship between neural recordings. The results show that the present framework provides a novel non-parametric method to estimate both time and frequency domain measurements which is applicable to neural spike trains.

We describe the main properties of the EQUATOR airborne geophysical system and how the system can be employed to solve some engineering and environmental problems. To date, the EQUATOR system has been used for groundwater search in 2014 and 2015 in Siberia. The name “EQUATOR” originates from the intention to EQUATE, or to balance time and frequency domain approaches in airborne electromagnetics, which is why the system was originally designed. To the best of our knowledge, EQUATOR is the only system to collect electromagnetic data both in time and in frequency domains. We further explain the difference between the two methods on some numerical and field examples. Also we describe some advantages of their coupling. In particular, by virtue of our approach, a numerically more robust solution of the one-dimensional electromagnetic inverse problem may be achieved. The recorded full-time measurements are further converted to in-phase, quadrature and off-time responses, as opposed to standard frequency or time domain solutions, which have been widely adapted within the industry. When inverting the data, our main focus is within the top 100 m of the Earth, which are the most relevant for engineering and environmental studies. We emphasize that a combined use of frequency and time domain data gives more detailed information about geological structures in the near-surface. As an example, we present some case studies obtained over a platform (Siberia) and a crystalline shield (Western Africa) setting. We further suggest using these results in such near-surface studies as sediment thickness estimation, soil state analysis, groundwater search, and geological structural analysis.

Conventional time-frequency Transformers primarily focus on the global features of signals in the time-frequency domain while neglecting the local features in both the time domain and frequency domain. This limitation hinders the ability of the model to effectively capture the shared features among time, frequency, and time-frequency representations. To address this issue, a Multi-Branch Joint Time-Frequency Transformer (MBJTF-Transformer) is proposed for domain generalization (DG) fault diagnosis of rotating machinery. Specifically, a time-branch Transformer is designed to extract temporal features, while a frequency-branch Transformer captures frequency-domain information. In addition, a time-frequency Transformer is employed to learn the shared representations across time, frequency, and time-frequency domains. Finally, a multi-decision fusion strategy of MBJTF-Transformer is adopted to enhance the generalization capability of the model. Experimental results on both the SCARA (Selective Compliance Assembly Robot Arm, SCARA) dataset and the PU (Paderborn University) bearing dataset demonstrate that the proposed MBJTF-Transformer achieves superior DG performance compared to multiple state-of-the-art sequential models.

The essential task of a Brain-Computer Interface (BCI) is to extract the motor imagery features from Electro-Encephalogram (EEG) signals for classifying the thought process. It is necessary to analyse these obtained signals in both the time domain and frequency domains. It is observed that the combination of multiple algorithms increases the performance of the feature extraction process. This paper identifies combinations that have not been attempted previously and improves the accuracy of the overall process, although other authors implemented different combinations of the techniques. The focus is given more on the feature extraction process and frequency bands, which are user-specific and subject-specific frequency bands. In both time and frequency domains, after analysing EEG signals with the time domain parameter, we select the frequency band and the timing while using the Fisher ratio of the time domain parameter (TDP). We used Fisher discriminant analysis (FDA)-type F-score to simultaneously select the frequency band and time segment for multi-class classification. We extracted subject-specific TDP features from the training trials to train the classifier when optimal time-frequency areas were selected for each subject. In this paper, various methods are explored for obtaining the features, which are Time Domain Parameters (TDP), Fast Fourier Transform (FFT), Principal Component Analysis (PCA), R2, Fast Correlation Based Filter (FCBF), Empirical Mode Decomposition (EMD), and Intrinsic time-scale decomposition (ITD). After the extraction process, PCA is used for dimensionality reduction. An efficient result was obtained with the combination of TDP, FFT, and PCA. We used the multi-class Fisher’s linear discriminant analysis (LDA) as the classifier, which was in line with the FDA-type F-score. It is observed that the combination of feature extraction techniques to the frequency bands that were selected by the Fisher ratio and FDA type F-score along with Fisher’s LDA classifier had higher accuracy than the results obtained other researches. A kappa coefficient accuracy of 0.64 is obtained for the proposed technique. Our method leads to better classification performance when compared to state-of-the-art methods. The novelty of the approach is based on the combination of frequency bands and two feature extraction methods.

Multiple access schemes for Cognitive Radio networks: A survey

Heart period variability has been considered for clinical assessment of autonomic function, determining the presence of haemorrhage or disease states, and for predicting mortality from traumatic injury. However, for heart period variability to be clinically useful, a number of important methodological issues should be addressed, including the minimum number of R-R intervals (RRI) required for accurate derivation, and the reproducibility of these metrics. ECGs were recorded for > or =10 min in 18 resting, supine subjects (12 M/6 F; 19-55 years). Heart period variability analyses included 21 time, frequency and complexity domain metrics. For assessment of minimum RRIs required, measurements were made from ECG recordings of 5 min down to 30 s for time and frequency domain metrics, and from 800 RRIs down to 100 RRIs for complexity metrics, by methodical truncation of the data set. Inter-subject variability was assessed by calculating the range and co-efficient of variation (%CV) across all subjects. Two independent 30 s or 100 RRI ECG segments were used to assess intra-subject variability via calculation of %CV in each subject. Six time and frequency domain metrics were robust down to 30 s of data, while five complexity metrics were robust down to 100 RRIs. All time and frequency domain metrics (except for RRI) exhibited high inter-subject variability (CVs > or = 30.0%), while five of eleven complexity metrics displayed low inter-subject variability (CVs < or = 8.5%). In the assessment of intra-subject variability in metrics valid with 30 s or 100 RRIs of ECG, only one time domain and four complexity metrics had CVs < 10%. Metrics that are highly reproducible and require few RRIs are advantageous for patient monitoring as less time is required to assess physiological status and initiate early interventions. Based on our analyses from healthy, resting humans, we have identified a select cohort of heart period variability metrics that performed well in regards to these two criteria.

In this article, theoretical modeling and simulations are introduced to explore the contact failure characteristics for connectors both in time and frequency domains. The equivalent models are developed to describe the impedance performances of un‐degraded and degraded connectors with the time domain and frequency domain, respectively. The experiments using a Network Analyzer for time and frequency domains analyses are conducted to verify the proposed models. The measured results agree well with the simulation results in the time and frequency domains.

The multicarrier code division multiple access (MC-CDMA) system has received a considerable attention from researchers owing to its great potential in achieving high data rates transmission in wireless communications. Due to the detrimental effects of multipath fading the performance of the system degrades. Similarly, the impact of non-orthogonality of spreading codes can exist and cause interference. This paper addresses the performance of multicarrier code division multiple access system under the influence of frequency selective generalized η-µ fading channel and multiple access interference caused by other active users to the desired one. We apply Gaussian approximation technique to analyse the performance of the system. The avearge bit error rate is derived and expressed in Gauss hypergeometic functions. Maximal ratio combining diversity technique is utilized to alleviate the deleterious effect of multipath fading. We observed that the system performance improves when the parameter η increase or decreasse in format 1 or format 2 conditions respectively.

The capacity of CDMA (code-division multiple access) systems is limited by its other-user interference because all users in a cell share the same frequency band. To achieve maximum capacity, the 'near-far' problem needs to be dealt with. In practice, this is done through appropriate power control. Due to the interaction between the power control process, CDMA systems may become unstable if the desired SNR is not set appropriately. When CDMA systems are stable, the received power at the base station converges to the optimal power with a desired SNR (signal-to-noise ratio) setting. We analyze the associated power and the interference estimation in the discrete time domain and find the stability SNR upper bounds that can guarantee the system stability.

An improved mathematical model describing acoustic emission (AE) signals generated by different types of partial discharges (PD) that occur in electric power transformer insulation system is presented in the paper. AE signals are analyzed within the AE method as applied for power transformer failure detection due to occurrence of PD. There are several types of basic defects, which are characterized by different types of PD. The mathematical model presented here is crucial for numerical analyses and simulations, where it acts as the function describing the acoustic source in an acoustic model of power transformer insulation system. The regression procedure was performed based on empirical AE signals, registered in a laboratory experiment. The AE signals are described by a mathematical model being a multi-parameter function, which involve both the time domain and the frequency domain. Goodness of the model was evaluated based on analysis of 480 data samples in the time, frequency and time-frequency domains. Also coherence between the registered and modeled signals was calculated. It was stated that the improved model fits very well to the real data, although, due to high level of noise embodied in signals registered in experiments, the coherence values remain low. Moreover, analyses of the estimated data were performed and some example results are presented in this paper. Based on the achieved outcomes a collection of parameter values was prepared for each of the eight considered PD basic types. One can simple use it now in a numerical model for simulation of AE signal source generated by specified type of PD, what corresponds to a particular power transformer insulation system failure. Furthermore, the regression procedure presented in this paper can be easily transferred to any other types of AE sources including processes of compression, tension and cracking.

A comparative analysis of electromyogram (EMG) signals of patients of cervical pain and normal controls. To determine the differences between frequency and time domain parameters of EMG signals of patients of cervical pain and normal controls. No diagnostic technique has emerged as a satisfactory tool for identification of spinal pain. Seventeen male and 17 female chronic neck pain patients without cervical radiculopathy were recruited through neurology EMG clinic. The controls consisted of 30 male and 33 female subjects with no history of neck pain in the past 12 months. All subjects performed flexion, left anterolateral flexion, left lateral flexion, left posterolateral extension, and extension to pain threshold/20% maximum voluntary contraction and pain tolerance/maximum voluntary contraction in random order. The descriptive statistics for body weight normalized strength, normalized peak EMG, time to onset, time to peak, median frequency, mean power frequency, and frequency bands were calculated. These variables were subjected to analysis of variance and logistic regression to distinguish between patients and controls. The normalized peak EMG of patients was significantly greater than those of controls in both maximal and submaximal exertions (P < 0.01). Whereas there was no consistent pattern in time to peak EMG, the time to onset of EMG revealed that the left sternocleidomastoid was always recruited before the onset of torque. A lack of significant difference in the median frequency of the 2 samples indicates that the pain did not disturb the muscle conduction velocity. Using discriminant logistic regression on frequency domain and time domain parameters, up to 97% of patients and controls were correctly classified with the resubstitution method. Surface EMG can be used successfully in distinguishing chronic pain patients and controls, and efficacy of treatment regimes.

We describe the main properties of the EQUATOR airborne electromagnetic system and how the system is employed to process data in both time- and frequency domains. To the best of our knowledge, EQUATOR is the only system to collect electromagnetic data both in time and in frequency domains. We explain the difference between the two methods on some numerical and field examples. Also, we describe some advantages of their coupling. In particular, by virtue of our approach, a numerically more robust solution of one-dimensional electromagnetic inverse problem may be achieved. The recorded full-time measurements are further converted to in-phase, quadrature and off-time responses, as opposed to standard frequency or time domain solutions, which have been widely adapted within the industry. When inverting the data, our main focus is within the top 200 m of the Earth. We emphasize that a combined use of frequency and time domain data gives more detailed information about geological structures in the near-surface.

Influence of water activity on acoustic emission of flat extruded bread

Time domain reflectometry (TDR) is finding increased applications for civil engineering field instrumentation. New applications are being identified based on innovative design of TDR sensor system. Numerical simulation plays an important role for guiding and evaluation of these new designs. This paper discusses the implementation of numerical simulation of frequency domain and time domain models of nonuniform TDR system. The frequency domain model is based on the concept of impedance transform, which can easily incorporate the dispersive characteristics of construction materials. The time domain simulation is carried out by FDTD method. Results of simulation based on both frequency domain model and time domain FDTD method are compared to identify the relative merits of these numerical methods. The simulation of TDR system responses establish the basis for aiding TDR sensor system design and is incorporated into the inversion procedures to be investigated in the future.