Signal Length Research Articles

Deep RP-CNN for Burst Signal Detection in Cognitive Radios

This article proposes a convolutional neural network (CNN)-based signal detection scheme using image encoding techniques for burst signals in wireless networks. The conventional signal detection approach based on energy measurement performs poorly when detecting burst signals owing to the short signal length and relatively long sensing duration. To detect the presence of a burst signal, the proposed scheme encodes the received time-series signal into an image that is further fed to a CNN model. For image encoding techniques, recurrence plot algorithms are adopted in the proposed scheme with a CNN. In particular, the proposed scheme achieves the correct detection probability of 99% even in the presence of a short burst signal at SNR= -10 dB.

Automatic Modulation Classification Using a Deep Multi-Stream Neural Network

In wireless communication, modulation classification is an important part of the non-cooperative communication, and it is difficult to classify the various modulation schemes using conventional methods. The deep learning network has been used to handle the problem and acquire good results. In the deep convolutional neural network (CNN), the data length in the input is fixed. However, the signal length varies in communication, and it causes that the network cannot take advantage of the input signal data to improve the classification accuracy. In this paper, a novel deep network method using a multi-stream structure is proposed. The multi-stream network form increases the network width, and enriches the types of signal features extracted. The superposition convolutional unit in each stream can further improve the classification performance, while the shallower network form is easier to train for avoiding the over-fitting problem. Further, we show that the proposed method can learn more features of the signal data, and it is also shown to be superior to common deep networks.

Performance Analysis and Evaluation of Frequency-Locked Loop for Weak GNSS Signals Based on Spectral Line Interpolation

In order to track weak signals, GNSS receivers often use a frequency-locked loop (FLL) to track the carrier frequency. The performance of the frequency discriminator directly affects the receiver's signal tracking capability in the FLL. In weak signals, FFT is currently used to estimate the carrier frequency. However, the FFT has a fence effect. The signal length affects the accuracy of the frequency estimation. According to the problem of FFT frequency estimation with low accuracy, we introduce three frequency estimators, which have good frequency estimation performances under low signal-to-noise ratio for GNSS weak signal tracking and propose a carrier frequency tracking method. The article compares and analyzes the estimation error performance of different frequency discriminators in detail. The simulation experiments are used to verify and analyze the tracking sensitivity of the tracking loop under different carrier-to-noise ratios. Theoretical analysis and simulation results show that these estimators effectively improve the accuracy of frequency estimation compared to traditional FFT estimator. Using the open-loop tracking structure, the tracking sensitivity can reach 24 dB-Hz when the coherent integration time is 20 ms and the number of signal samples is 16. Adding a loop filter, the tracking threshold can be improved to 20 dB-Hz.

Cognitive Multi-Point Free Space Optical Communication: Real-Time Users Discovery Using Unsupervised Machine Learning

Multi-user free-space optical communication (FSOC) is beginning to draw a significant attention for its ability to support increased system network capacity while using single receiving photodiode and satisfying size, weight, and power (SWaP) constraints imposed by space- and aerial-based mobile communication. Despite these advantages, support of multi-user capabilities cause increased system complexity due to accommodating heterogenous users communications with varying transmission and data rate requirements. Machine learning (ML) has recently been considered as a promising approach for introducing cognition into the network to mitigate some of the complexity. A cognitive method based on unsupervised ML was derived for estimating the number of users communicating and sharing time and bandwidth resources with a single-node receiver. A weighted clustering approach was introduced and experimentally validated when users received with similar amplitude information that results in underestimation. Obtained results confirmed that the proposed methodology was able to accurately differentiate the number of simultaneously transmitting users with accuracy greater than 92%— even in the presence of moderate atmospheric turbulence. An experimental analysis was conducted to determine data size and receiver sampling rate requirements for accurate estimation. Furthermore, an empirical model was derived to evaluate the effect of preamble signal length given a particular sampling rate on the accuracy of the estimation. The model was validated for up-to four users.

Enhancing the Bitrate and Power Spectral Density of PPM TH-IR UWB Signals using a Sub-Slot Technique

Increasing the receiver’s bitrate and suppressing the spectral line are issues of major interest in the design of compliant Time-Hopping Impulse Radio (TH-IR) Ultra-Wide Band (UWB) systems. Suppression of spectral lines has been commonly addressed by randomizing the position of each pulse to make the period as large as possible. Our analysis suggests that this influences the overall shape of a signal’s Power Spectral Density (PSD) in a way that is useful for spectral line suppression or diminishing the PSD maximum peak power. A method for utilizing the system to generate a Dynamic-Location Pulse-Position Modulated (DLPPM) signal for transmission across a UWB communications channel is presented, and an analytical derivation of the PSD of a proposed DLPPM signal TH-IR UWB is introduced. Our proposed method can be applied without affecting the users of other concurrent applications. The theoretical model for DPLM TH-IR is compared with the PSD for conventional DPLM TH-IR. The results show that spectral estimation methods based on Fast Fourier Transform (FFT) significantly overestimate the continuous part of the PSD for small and medium signal lengths, which has implications for assessing interference margins by means of simulation. Another purpose of this paper is to improve a predesigned system by increasing the receiver’s bitrate. This will be achieved by using the bits that control the sub-slot technique as information and designing a receiver capable of detecting them. The bitrate is effectively doubled. Finally, the proposed system for DPLM TH-IR has been built inside Simulink/MATLAB to test its results via a conventional DPLM TH-IR system.

Androgenic modulation of extraordinary muscle speed creates a performance trade-off with endurance.

Performance trade-offs can dramatically alter an organism's evolutionary trajectory by making certain phenotypic outcomes unattainable. Understanding how these trade-offs arise from an animal's design is therefore an important goal of biology. To explore this topic, we studied how androgenic hormones, which regulate skeletal muscle function, influence performance trade-offs relevant to different components of complex reproductive behaviour. We conducted this work in golden-collared manakins (Manacus vitellinus), a neotropical bird in which males court females by rapidly snapping their wings together above their back. Androgens help mediate this behavior by radically increasing the twitch speed of a dorsal wing muscle (scapulohumeralis caudalis, SH), which actuates the bird's wing-snap. Through hormone manipulations and in situ muscle recordings, we tested how these positive effects on SH speed influence trade-offs with endurance. Indeed, this latter trait impacts the display by shaping signal length. We found that androgen-dependent increases in SH speed incur a cost to endurance, particularly when this muscle performs at its functional limits. Moreover, when behavioural data were overlaid on our muscle recordings, displaying animals appeared to balance display speed with fatigue-induced muscle fusion (physiological tetanus) to generate the fastest possible signal while maintaining an appropriate signal duration. Our results point to androgen action as a functional trigger of trade-offs in sexual performance - these hormones enhance one element of a courtship display, but in doing so, impede another.

Intelligent recognition of severe slugging in a long-distance pipeline-riser system

In this work, experiments on flow regimes during two-phase flow in a long-distance pipeline-riser system were carried out. The test loop with inner diameter 46 mm consists of a long horizontal pipeline with length 1657 m, followed by a 30 m downward inclined section, and ended at an S-shaped riser with height 11.2 m. A simple method utilizing pressure signals and artificial neural networks is proposed to identify the flow regimes. Firstly, characteristic parameters of pressure signals in time-domain and frequency-domain are extracted, then they are reduced by principal component analysis, and finally flow regimes are recognized by artificial neural networks. The influence of pressure signals at different positions along the pipeline, which includes the horizontal section, the inclined section, the riser section and the separator, on the overall recognition rate is discussed. Higher recognition rates can be achieved by using pressure signals at and near the bottom of the riser rather than pressure signals at other locations along the pipeline. Additionally, a higher recognition rate can be obtained using the pressure difference of the riser instead of the pressure signal. Based on pressure signals at and near the bottom of the riser, when flow regimes are divided into two categories, a satisfactory recognition rate of 93% can be obtained with the signal length of 60 s; When the signal length exceeds 120 s, the recognition rate is about 96%, and it remains unchanged as the signal length increases; When flow regimes are classified into four categories, the recognition rate is about 90% if the signal length exceeds 120 s and remains unchanged as the signal length increases.

Adaptive Noise Cancellation from Speech Signals using Variablestep Sizealgorithm

Noise cancellation from the speech signal is the most importanttask in applications like communications, hearing aids, speech therapy and many others. This allows providing good resolution speech signal to the user. The speech signals are mostlycontaminated due to the several natural as well as manmade noises. As the characteristics of these noises random in its nature filtering techniques with fixed coefficients are not suitable for noise cancellation task. Hence, in this work an adaptive noise canceller algorithmhas driven for enhancement of speech signal applications which has the capability to update its weight coefficients based on the statistical nature of the undesired component in the actual speech signal. In our experiments in order to achieve better convergence rate as well as filtering capability we propose Step Variable Least Mean Square (SVLMS) algorithm instead of constant step parameter. The computational complexity of the speech enhancement process is also a key aspect due to the excessive length of the speech signals in realistic scenario. Hence, to reduce the computational complexity of the proposed mechanism we used Sign Regressor SVLSM (SRSVLMS), which is a hybrid realization of familiar sign regressor algorithm and the proposed SVLMS. Using these two techniques noise cancellation models are developed and tested on real speech signals with unwanted noise contaminations. The experimental outputsconfirm that the SRSVLMS based speech signal enhancement unit performs better than its counterpart with respect to convergence rate, computational complexity and signal to noise ratio increment.

Assessment of Deep Brain Stimulation Implantation Surgery: A Practical Scale

Patients requiring deep brain stimulation (DBS) will undergo extensive preoperative and postoperative evaluations. However, the field lacks a robust scoring system for quantifying the outcomes of DBS surgery. We sought to determine whether a practical scale could assess the outcomes of DBS surgery and the clinical significance. A retrospective study was performed of the data from 150 patients who had undergone DBS from February 2017 to February 2019. An independence analysis and multivariate testing were used to identify significant independent predictors. The scale scores were computed by summing across the weighted predictors. The correlation between the scale scores and the intraoperative electrophysiological signal length (IESL), DBS power-on voltage, improvement rate in the unified Parkinson disease rating scale (UPDRS) and UPDRS part III (UPDRS III) scores was analyzed. Receiver operating characteristics curve analysis was used to quantify the discriminative capacity of the scale for predicting the prognosis. Listwise exclusion of patients with incomplete data sets yielded a final sample of 130 patients with Parkinson disease who had undergone bilateral DBS. Multivariate testing identified 3 independent predictors of the prognosis, including electrode implantation duration, postoperative pneumocephalus volume, and electrode fusion error. The scale scores correlated significantly with the subthalamic nucleus DBS power-on voltage (r=-0.4063; P < 0.0001), globus pallidus internus DBS power-on voltage (r=-0.4723; P= 0.0014), and improvement rate of the UPDRS (r= 0.3490; P < 0.0001) and UPDRS III (r= 0.6623; P < 0.0001) scores. However, the scale scores did not significantly correlate with the subthalamic nucleus IESL and globus pallidus internus IESL. Receiver operating characteristics curve analysis revealed impressive outcome discrimination for the UPDRS and UPDRS III scores (UPDRS: area under the curve, 0.62, P= 0.0219; UPDRS III: area under the curve, 0.85, P < 0.0001). We have introduced a novel practical scale capable of assessing the outcomes of DBS surgery and predicting the prognosis of patients after DBS surgery.

Bsense: Practical Cross-Technology Communication Utilizing Beacon Frames of Commodity WiFi APs

A considerable number of wireless communication devices can share a single radio frequency. Cross-technology communication is a novel approach that enables direct communication between heterogeneous communication devices that share a limited frequency. This article presents Bsense, a solution designed for practical cross-technology communication. We first investigate the empirical challenges to such a technology in the real world. Bsense offers practical solutions to overcome these challenges without specialized hardware or standards modification. The key concept of Bsense is to distinguish between symbols using differences in the signal strength or the signal length of periodic IEEE 802.11 beacons. We have implemented Bsense on a commodity WiFi access point and CC2650 SensorTag. Real-world experimental evaluations in terms of throughput, symbol error rate, and duty cycle demonstrate that Bsense provides sufficient performance for practical cross-technology communication. Bsense is expected to be applicable to new IoT applications because of its compatibility and applicable performance with respect to commodity communication technologies.

Numerical investigation on mixing intensification of ferrofluid and deionized water inside a microchannel using magnetic actuation generated by embedded microcoils for lab-on-chip systems

Effective and rapid mixing is crucial for chemical and biological processes. The purpose of the current study is to investigate the effect of steady and varying magnetic field on the mixing of a water-based ferrofluid and two streams of deionized water inside a microchannel for Lab-on-Chip applications. To this end, the nonlinear governing equations, the momentum equation, the continuity equation, the mass transport equation and the Maxwell-Ampere equations are numerically solved. A commercial code based on the finite-element method is used and the numerical simulations are validated by the experimental results in the literature. To augment the mixing performance, the effects of influencing parameters such as the magnetic field strength and frequency, inlet velocity, relative inlet velocity, and duty cycle (the ratio of signal activation on the total signal length) are investigated. A mixing efficiency as high as 94.4% only after 13 s is achieved with a peak current of 1 A, a frequency of 5 Hz, 100 μm/s of inlet velocity, a relative inlet velocity between the ferrofluid and water streams of 0.8 and a duty cycle of 0.2. This proposed model provides a versatile and low-cost solution to intensify microfluidic mixing performance and develop efficient Lab-on-Chip platforms.

An Optimized Approach to Pipelined Architecture for Fast 2D Normalized Cross-Correlation

An optimized hardware architecture for fast normalized cross-correlation (NCC) is essential in real-time high-speed applications. Typical applications of NCC are in object localization, as one of the best motion estimators and as a similarity measure in the field of image processing. However, high computational cost is a significant drawback of NCC. To reduce computation time and hardware resource usages, this paper presents a feed-forward single-path architecture with parallel computation of numerator and denominator components of NCC. The cross-correlation of two signals is implemented in the frequency domain using pipelined architecture of 2D FFT with polyphase sequential subband decomposition technique. The FFT can be determined for any even length of signal when compared to the traditional method involving a power of two-signal length. The proposed pipelined architecture of NCC is more efficient in terms of computational complexity and memory requirement. This proposed architecture is implemented in Verilog HDL with fixed-point data type which helps to devise simple and efficient architecture, which gives excellent SQNR of more than 90 dB with 22-bit output word length. It can operate at the maximum clock frequency of 220.4[Formula: see text]MHz, takes a total NCC time of 4.36[Formula: see text][Formula: see text]s and has a latency of 996 cycles, giving a throughput of 220 Msamples/s for a block size of [Formula: see text] pixels using Virtex-7 FPGA. This pipelined architecture not only offers an attractive solution for different sizes of image block, but also improves the speed of the system.

On the reducing of the acoustic signal length from the piezoelectric transducers of the sea ice monitoring systems

The solution of navigation problems the ice situation problems in the Arctic region need to study the acoustic parameters and local reflective properties of ice in real conditions. Information about the local sea ice acoustic properties essentially helps to solve the problems of statistical forecasting of reflective and scattering properties of ice cover. The successful solution of these problems mostly depends on the metrological parameters of the probing signals emitted by the primary piezoelectric transducers. This means, that to increase the resolution of the control and measuring equipment it is better to radiate and to receive short acoustic pulses. The article presents the results of analysis the pulse mode of operation of cylindrical piezoelectric transducers designed to measure the velocity of sound in sea ice. To reduce the length of acoustic pulses, an inductive-resistive load is connected to the electrical input of the radiator. The length and the amplitude of the emitted acoustic pulse were estimated using equivalent circuits of piezoelectric transducers and a spectral method based on the Fourier transforms. The optimal parameters of the electrical circuit, ensuring the minimum length of the acoustic pulses are calculated. The technique of possible measurements is described. The results are presented in the most general form, making possible to use them within different frequency ranges.

Total least square clutter cancellation in passive radar

In this study, a total least square (TLS) based cancellation method is proposed for suppressing direct and multipath clutter signals in passive radar. The TLS algorithm is first used to estimate the distribution of the clutter signals over delay bins. Then the clutter signal subspace is constructed by delaying the reference signal according to the estimated number and delay bins of the clutter signals. Finally, the surveillance signal is projected into the orthogonal subspace of the clutter space. Compared to the typical extended cancellation algorithm, the proposed method uses a smaller but more accurate order of clutter space, so it can get accurate estimate of the clutter amplitudes by only using a limited length of signals. This indicates the proposed method is with the potential to suppress time-variant interference where only a small number of signal samples is available.

Fractional Refined Composite Multiscale Fuzzy Entropy of International Stock Indices

Fractional refined composite multiscale fuzzy entropy (FRCMFE), which aims to relieve the large fluctuation of fuzzy entropy (FuzzyEn) measure and significantly discriminate different short-term financial time series with noise, is proposed to quantify the complexity dynamics of the international stock indices in the paper. To comprehend the FRCMFE, the complexity analyses of Gaussian white noise with different signal lengths, the random logarithmic returns and volatility series of the international stock indices are comparatively performed with multiscale fuzzy entropy (MFE), composite multiscale fuzzy entropy (CMFE) and refined composite multiscale fuzzy entropy (RCMFE). The empirical results show that the FRCMFE measure outperforms the traditional methods to some extent.

Monitoring Severe Slugging in Pipeline-Riser System Using Accelerometers for Application in Early Recognition.

The use of accelerometer signals for early recognition of severe slugging is investigated in a pipeline-riser system conveying an air–water two-phase flow, where six accelerometers are installed from the bottom to the top of the riser. Twelve different environmental conditions are produced by changing water and gas superficial velocities, of which three conditions are stable states and the other conditions are related to severe slugging. For online recognition, simple parameters using statistics and linear prediction coefficients are employed to extract useful features. Binary classification to recognize stable flow and severe slugging is performed using a support vector machine and a neural network. In multiclass classification, the neural network is adopted to identify four flow patterns of stable state, two types of severe slugging, and an irregular transition state between severe slugging and dual-frequency severe slugging. The performance is compared and analyzed according to the signal length for three cases of sensor location: six accelerometers, one accelerometer at the riser base, and one accelerometer at the top of the riser.

Gait symmetry methods: Comparison of waveform-based Methods and recommendation for use

Gait symmetry has been shown to be a relevant measure for differentiating between normal and pathological gait. Although a number of symmetry methods exist, it is not clear which of these methods should be used as they have been developed using data collected from varying experimental protocols. This paper presents a comparison of state-of-the-art waveform-based symmetry methods and tests them on walking data collected from different environments. Acceleration signals collected from the ankle are used to analyse symmetry methods under different signal circumstances, such as phase shift, waveform shape difference, signal length (i.e. number of gait cycles) and gait initiation phase. The cyclogram based method is invariant to signal phase shifts, signal length and the gait initiation phase. The trend symmetry method is not affected by signal scaling and the gait initiation phase but is affected by signal length depending on the environment. Similar to the trend method, the cross-correlation symmetry method is not responsive to signal scaling and the gait initiation phase. The results of the symbolic method are not influenced by signal scaling, gait initiation and depending on the environment by the signal phase shift. From the results of the performed analysis, we recommend the trend method to gait symmetry assessment. The comparison of waveform-based symmetry methods brings new knowledge that will help in selecting an appropriate method for gait symmetry assessment under different experimental protocols.

Research on an Optimization Method for a Partially Responsive Continuous Phase Modulated (CPM) Signal Based on an Optimal Generic Function

This paper establishes an optimal generic function model in order to obtain a continuous phase modulated (CPM) signal with a smoother phase modulation function. This is achieved by finding the solution to the symbol signals at different lengths of the CPM function. In the solution process, the unknown amount that needs to be solved is reduced by using the even function symmetry characteristic of the signal to be solved. For each different form of the signal, the time domain form of the CPM function and the corresponding normalized energy spectral density are compared under the influence of the phase modulation signal length and the generic function parameter n. The data transmission rate is improved by introducing inter-symbol interference, and the modulation process is realized using six-way parallel transmission when the CPM function is 6T. The simulation results show that the CPM function obtained by establishing an optimal generic function model has high-quality time-frequency characteristics. The real-time phase trajectory and the high-order derivative are both continuous, and the modulated signal has constant envelope characteristics. The CPM function has a fast rolling-off in the frequency domain and small out-of-band radiation, which greatly improves the characteristics of the frequency band utilization.

Inference on Long-Range Temporal Correlations in Human EEG Data

Detrended Fluctuation Analysis (DFA) is a statistical estimation algorithm used to assess long-range temporal dependence in neural time series. The algorithm produces a single number, the DFA exponent, that reflects the strength of long-range temporal correlations in the data. No methods have been developed to generate confidence intervals for the DFA exponent for a single time series segment. Thus, we present a statistical measure of uncertainty for the DFA exponent in electroencephalographic (EEG) data via application of a moving-block bootstrap (MBB). We tested the effect of three data characteristics on the DFA exponent: (1) time series length, (2) the presence of artifacts, and (3) the presence of discontinuities. We found that signal lengths of ∼5 minutes produced stable measurements of the DFA exponent and that the presence of artifacts positively biased DFA exponent distributions. In comparison, the impact of discontinuities was small, even those associated with artifact removal. We show that it is possible to combine a moving block bootstrap with DFA to obtain an accurate estimate of the DFA exponent as well as its associated confidence intervals in both simulated data and human EEG data. We applied the proposed method to human EEG data to (1) calculate a time-varying estimate of long-range temporal dependence during a sleep-wake cycle of a healthy infant and (2) compare pre- and post-treatment EEG data within individual subjects with pediatric epilepsy. Our proposed method enables dynamic tracking of the DFA exponent across the entire recording period and permits within-subject comparisons, expanding the utility of the DFA algorithm by providing a measure of certainty and formal tests of statistical significance for the estimation of long-range temporal dependence in neural data.

When iconicity stands in the way of abbreviation: No Zipfian effect for figurative signals.

Zipf’s law of abbreviation, relating more frequent signals to shorter signal lengths, applies to sounds in a variety of communication systems, both human and non-human. It also applies to writing systems: more frequent words tend to be encoded by less complex graphemes, even when grapheme complexity is decoupled from word length. This study documents an exception to this law of abbreviation. Observing European heraldic motifs, whose frequency of use was documented for the whole continent and over two large corpora (total N = 25115), one medieval, one early modern, we found that they do not obey a robust law of abbreviation. In our early modern corpus, motif complexity and motif frequency are positively, not negatively, correlated, a result driven by iconic motifs. In both our corpora, iconic motifs tend to be more frequent when more complex. They grew in popularity after the invention of printing. Our results suggest that lacking iconicity may be a precondition for a graphic code to exhibit Zipf’s Law of Abbreviation.