Signal Detection Method Research Articles

Weak photoacoustic signal detection based on the differential duffing oscillator

In view of photoacoustic spectroscopy theory, the relationship between weak photoacoustic signal and gas concentration is described. The studies, on the principle of Duffing oscillator for identifying state transition as well as determining the threshold value, have proven the feasibility of applying the Duffing oscillator in weak signal detection. An improved differential Duffing oscillator is proposed to identify weak signals with any frequency and ameliorate the signal-to-noise ratio. The analytical methods and numerical experiments of the novel model are introduced in detail to confirm its superiority. Then the signal detection system of weak photoacoustic based on differential Duffing oscillator is constructed, it is the first time that the weak signal detection method with differential Duffing oscillator is applied triumphantly in photoacoustic spectroscopy gas monitoring technology.

Auto measurement while drilling mud pulse signal recognition based on deep neural network

The noise intensity of drilling mud pulse signal is large. It is difficult to recognize signal at once because of the low signal to noise ratio (SNR) of the detected signals. Moreover, the SNR has a low accuracy. A method which is called stacked wavelet autoencoder (SWAE) for recognizing mud pulse signals based on deep learning is proposed in this paper. The model is composed of wavelet neural network and autoencoder, which are trained for mud pulse signal classification specifically. Combining with the drilling mud pulse signal, the recognition performance of the typical data set is analyzed and tested. SWAE enhances the SNR of output signal by using signal detection method. And then the output detected signal is considered as a signal classification attribute. Finally, experimental results show that SWAE is suitable for mud pulse signal recognition, and it has strong ability to extract features from samples automatically and robustness. Experimental results confirm that the performance of SWAE is better than the methods in the state of the art under the same SNR.

BLE Beacons for Internet of Things Applications: Survey, Challenges, and Opportunities

While the Internet of Things (IoT) is driving a transformation of current society toward a smarter one, new challenges and opportunities have arisen to accommodate the demands of IoT development. Low power wireless devices are, undoubtedly, the most viable solution for diverse IoT use cases. Among such devices, Bluetooth low energy (BLE) beacons have emerged as one of the most promising due to the ubiquity of Bluetooth-compatible devices, such as iPhones and Android smartphones. However, for BLE beacons to continue penetrating the IoT ecosystem in a holistic manner, interdisciplinary research is needed to ensure seamless integration. This paper consolidates the information on the state-of-the-art BLE beacon, from its application and deployment cases, hardware requirements, and casing design to its software and protocol design, and it delivers a timely review of the related research challenges. In particular, the BLE beacon’s cutting-edge applications, the interoperability between packet profiles, the reliability of its signal detection and distance estimation methods, the sustainability of its low energy, and its deployment constraints are discussed to identify research opportunities and directions.

The Design of Digital Frequency-Shift Signal Detection Method Based on FPGA

In this paper, the design process of a portable railway frequency shift signal detection system is introduced in detail. With the parallelism of FPGA, the multitask can be carried out simultaneously. Two channels are designed to detect the two signals simultaneously. With the help of gear switching circuit, the measurement range and measurement accuracy of the system are greatly improved. Secondly, the FIR filter bank is designed based on FPGA, the signal is filtered and analyzed, the amplitude and frequency of the signal are detected, and the detection result is displayed in real time by using LCD.

Efficient Signal Detection Methods for High Mobility OFDM System with Transmit Diversity

In this paper, we propose two efficient signal detection methods for high mobility space–time block code based orthogonal frequency division multiplexing (OFDM). The first proposed method is based on order block decision feedback equalizer (OBDFE) which is the column norm ordering version of the conventional BDFE method. To improve the system performance further, we have proposed a hybrid OBDFE–MPD method by cascading the OBDFE method with a multistage parallel decoder (MPD). The advantage of the OBDFE–MPD method is that it does not need additional complexity as most of the computations are performed in the initial stage by the OBDFE method. These two proposed methods are compared with minimum mean square error (MMSE), block linear minimum mean square error (BLMMSE), block decision feedback equalizer (BDFE), vertical Bell laboratories layered space–time (VBLAST) and space alternating generalized expectation maximization (SAGE) in terms of performance and computational complexity. From the simulation results and computational complexity, it is shown that both the proposed OBDFE and OBDFE–MPD methods provide a great trade-off between performance and computational complexity for high mobility OFDM system with transmit diversity.

Weak wide-band signal detection method based on small-scale periodic state of Duffing oscillator**Project supported by the National Natural Science Foundation of China (Grant No. 61673066).

The conventional Duffing oscillator weak signal detection method, which is based on a strong reference signal, has inherent deficiencies. To address these issues, the characteristics of the Duffing oscillatorʼs phase trajectory in a small-scale periodic state are analyzed by introducing the theory of stopping oscillation system. Based on this approach, a novel Duffing oscillator weak wide-band signal detection method is proposed. In this novel method, the reference signal is discarded, and the to-be-detected signal is directly used as a driving force. By calculating the cosine function of a phase space angle, a single Duffing oscillator can be used for weak wide-band signal detection instead of an array of uncoupled Duffing oscillators. Simulation results indicate that, compared with the conventional Duffing oscillator detection method, this approach performs better in frequency detection intervals, and reduces the signal-to-noise ratio detection threshold, while improving the real-time performance of the system.

SDA-based Neural Network Approach for MWD Mud Pulse Signal Recognition

The low signal to noise ratio (SNR) of the detected mud pulse signal leads to difficult to recognize signal at once. The recognize accuracy is low. So a stacked denoising autoencoder recognition model was constructed. Combining with the drilling mud pulse signal, the recognition performance of the typical data set is analyzed and tested. The proposed method of recognizing mud pulse signal enhances the SNR of output signal by using signal detection method. And then we take the output detected signal as signal classification attribute. Test results also show that the proposed method is suitable for mud pulse signal recognition, and it has strong ability to extract features from samples automatically and robustness. Performance is better than pattern matching and support vector machine and other recognition methods under the same SNR.

Generation and coherent detection of QPSK signal using a novel method of digital signal processing

We demonstrate an optical quadrature phase-shift keying (QPSK) signal transmitter and an optical receiver for demodulating optical QPSK signal with homodyne detection and digital signal processing (DSP). DSP on the homodyne detection scheme is employed without locking the phase of the local oscillator (LO). In this paper, we present an extracting one-dimensional array of down-sampling method for reducing unwanted samples of constellation diagram measurement. Such a novel scheme embodies the following major advantages over the other conventional optical QPSK signal detection methods. First, this homodyne detection scheme does not need strict requirement on LO in comparison with linear optical sampling, such as having a flat spectral density and phase over the spectral support of the source under test. Second, the LabVIEW software is directly used for recovering the QPSK signal constellation without employing complex DSP circuit. Third, this scheme is applicable to multilevel modulation formats such as M-ary PSK and quadrature amplitude modulation (QAM) or higher speed signals by making minor changes.

Low-Complexity MIMO Signal Detection Employing Multistream Constrained Search

This study proposes the multi-stream constrained search (MSCS) as a low-complexity signal detection method for multiple-input multiple-output (MIMO) communications. MSCS achieves an excellent trade-off between computational complexity and bit error rate (BER). Based on the maximum $a posteriori$ probability (MAP) estimation, MSCS applies discrete optimization to some streams and continuous optimization to others. First, the discrete optimization constrains some streams to a certain set of modulation symbols. The continuous optimization is then used to identify the optimal continuous values of the other streams under this constrained condition and quantizes the results. A signal candidate comprises the quantized optimal values and constrained streams. Multiple constraint patterns result in multiple signal candidates, and sphere decoding (SD) identifies the detected signal as the one that maximizes the likelihood function. Limiting the number of constrained streams and applying SD can reduce computational complexity. In addition, MSCS selects constrained streams that have large variances of Gaussian distributions obtained from the MAP estimation. As a result of this stream selection method, MSCS can maintain an excellent BER performance even when the constrained streams are few in number. Computer simulations in 8-by-8 MIMO channels with modulation schemes of 16- and 64-QAM demonstrate that MSCS suffers degradation of merely 0.2 dB in average BER performance compared to the maximum likelihood detection (MLD). We also show that MSCS can achieve a similar average BER performance as that of the QR decomposition and M algorithm (QRM-MLD) while requiring less computational complexity. Under uncorrelated channels, the complexity of MSCS is less than 1/2 and 1/4 that of QRM-MLD in the case of 16- and 64-QAM, respectively. By contrast, simulations under channels with transmit-side spatial correlation show that when 16-QAM is used and average $E_{b}/N_{0}$ is equal to 18 dB, the complexity of MSCS ranges from 3/10 to 2/5 that of QRM-MLD. We also show that the complexity of MSCS ranges from 1/10 to 1/6 that of QRM-MLD when 64-QAM is used and the average $E_{b}/N_{0}$ is 23 dB.

Somatosensory modulation of perceptual vestibular detection.

Vestibular-multisensory interactions are essential for self-motion, navigation and postural stability. Despite evidence suggesting shared brain areas between vestibular and somatosensory inputs, no study has yet investigated whether somatosensory information influences vestibular perception. Here, we used signal detection methods to identify whether somatosensory stimulation might interact with vestibular events in a vestibular detection task. Participants were instructed to detect near-threshold vestibular roll-rotation sensations delivered by galvanic vestibular stimulation in one-half of experimental trials. A vibrotactile signal occurred to the index fingers of both hands in half of the trials, independent of vestibular signals. We found that vibrotactile somatosensory stimulation decreased perceptual vestibular sensitivity. The results are compatible with a gain regulation mechanism between vestibular and somatosensory modalities.

Strengthening standards, transparency, and collaboration to support medicine evaluation: Ten years of the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP).

Strengthening standards, transparency, and collaboration to support medicine evaluation: Ten years of the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP).

Heterodyne interferometric signal retrieval in photoacoustic spectroscopy.

A new heterodyne interferometric method for optical signal detection in photoacoustic or photothermal spectroscopy is demonstrated and characterized. It relies on using one laser beam for the photoacoustic excitation of the gas sample that creates refractive index changes along the beam path, while another laser beam is used to measure these changes. A heterodyne-based detection of path-length changes is presented that does not require the interferometer to be balanced or stabilized, which significantly simplifies the optical design. We discuss advantages of this new approach to photoacoustic signal detection and the new sensing arrangements that it enables. An open-path photoacoustic spectroscopy of carbon dioxide at 2003 nm and a novel sensing configuration that enables three-dimensional spatial gas distribution measurement are experimentally demonstrated.

A nonparametric method to detect increased frequencies of adverse drug reactions over time.

Signal detection is routinely applied to spontaneous report safety databases in the pharmaceutical industry and by regulators. As an example, methods that search for increases in the frequencies of known adverse drug reactions for a given drug are routinely applied, and the results are reported to the health authorities on a regular basis. Such methods need to be sensitive to detect true signals even when some of the adverse drug reactions are rare. The methods need to be specific and account for multiplicity to avoid false positive signals when the list of known adverse drug reactions is long. To apply them as part of a routine process, the methods also have to cope with very diverse drugs (increasing or decreasing number of cases over time, seasonal patterns, very safe drugs versus drugs for life-threatening diseases). In this paper, we develop new nonparametric signal detection methods, directed at detecting differences between a reporting and a reference period, or trends within a reporting period. These methods are based on bootstrap and permutation distributions, and they combine statistical significance with clinical relevance. We conducted a large simulation study to understand the operating characteristics of the methods. Our simulations show that the new methods have good power and control the family-wise error rate at the specified level. Overall, in all scenarios that we explored, the method performs much better than our current standard in terms of power, and it generates considerably less false positive signals as compared to the current standard.

Detectability of the impacts of ozone-depleting substances and greenhouse gases upon stratospheric ozone accounting for nonlinearities in historical forcings

Abstract. We perform a formal attribution study of upper- and lower-stratospheric ozone changes using observations together with simulations from the Whole Atmosphere Community Climate Model. Historical model simulations were used to estimate the zonal-mean response patterns (“fingerprints”) to combined forcing by ozone-depleting substances (ODSs) and well-mixed greenhouse gases (GHGs), as well as to the individual forcing by each factor. Trends in the similarity between the searched-for fingerprints and homogenized observations of stratospheric ozone were compared to trends in pattern similarity between the fingerprints and the internally and naturally generated variability inferred from long control runs. This yields estimated signal-to-noise (S∕N) ratios for each of the three fingerprints (ODS, GHG, and ODS + GHG). In both the upper stratosphere (defined in this paper as 1 to 10 hPa) and lower stratosphere (40 to 100 hPa), the spatial fingerprints of the ODS + GHG and ODS-only patterns were consistently detectable not only during the era of maximum ozone depletion but also throughout the observational record (1984–2016). We also develop a fingerprint attribution method to account for forcings whose time evolutions are markedly nonlinear over the observational record. When the nonlinearity of the time evolution of the ODS and ODS + GHG signals is accounted for, we find that the S∕N ratios obtained with the stratospheric ODS and ODS + GHG fingerprints are enhanced relative to standard linear trend analysis. Use of the nonlinear signal detection method also reduces the detection time – the estimate of the date at which ODS and GHG impacts on ozone can be formally identified. Furthermore, by explicitly considering nonlinear signal evolution, the complete observational record can be used in the S∕N analysis, without applying piecewise linear regression and introducing arbitrary break points. The GHG-driven fingerprint of ozone changes was not statistically identifiable in either the upper- or lower-stratospheric SWOOSH data, irrespective of the signal detection method used. In the WACCM simulations of future climate change, the GHG signal is statistically identifiable between 2020 and 2030. Our findings demonstrate the importance of continued stratospheric ozone monitoring to improve estimates of the contributions of ODS and GHG forcing to global changes in stratospheric ozone.

A Method of Multi-component Signal Detection Based on Differential Nonlinear Mode Decomposition

In order to detect the multi-component signal from the noise and chaos, a method based on the differential nonlinear mode decomposition (DNMD) is proposed in this paper. This new analysis approach applies the differential to the original signal. Then the nonlinear mode decomposition (NMD) is used to obtain a series of meaningful nonlinear modes, which has the advantage of extracting high frequency components with small amplitudes and learns from the superiority of NMD such as noise robust. Finally, the spectrum analysis is used to the decomposed components. The analysis of simulation signals and the real underwater signal is given to demonstrate the effectiveness of this method. Proposed method can detect multi-component signals of time-varying amplitude without fake frequency under the condition of noise and chaos. Compared with traditional decomposition methods, the peaks of Hilbert marginal spectrum of proposed method are sharper, and R2, R3 are higher.

A Novel Personalized Motion and Noise Artifact (MNA) Detection Method for Smartphone Photoplethysmograph (PPG) Signals.

Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, PPG signals are sensitive to motion and noise artifacts (MNAs) especially when they are obtained from smartphone cameras. Moreover, PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. Here, a concept of the probabilistic neural network (PNN) is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive (AR) model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.

Design and simulation of MIMO and massive MIMO for 5G mobile communication system

This study concentrates on the investigation of signal detection by using Zero Forcing (ZF), Minimum Mean Square Error (MMSE) and Beam Forming (BF), for a MIMO and massive MIMO system. Outcomes show that ZF is the modest method for signal detection, though BF gives better Bit Error Rate (BER) performance with some constraints in implementation, but MMSE implementation complexity is reduced by avoiding the matrix inversion in the receiver while sustaining the optimal performance as compared to other conventional methods. For several antennas at the base location, it is too difficult to implement the weight matrix for ZF, still, it is appropriate for BF with the benefit of decent digital signal algorithms. Performance of MIMO and massive MIMO using equalisation is investigated to get the throughput of the system (BER vs. SNR). Results indicate a significant improvement in BER for 16x16 as compared to 4x4, 8x1 and 8x8 antennas at the base station. The other parameters like capacity and Signal Interference Noise Ratio (SINR) are likewise examined for massive MIMO system.

A BP Neural Network-Based Communication Blind Signal Detection Method With Cyber-Physical-Social Systems

The development of wireless communication technologies will lead to an increase of rapid-time-variants featured occasions and physical occasions of small-data-size communication in network, and the modulation system diversification resulting from social group densification. Though traditional blind signal detection approaches have been improved by reducing their excessive reliance on data size, they still have the defects of various postulated conditions in new settings, complicated computation, unstable detection effect and high error rate. This paper aims to explore a communication blind signal detection method in the setting of cyber-physical-social systems in the field of back-propagation (BP) neural network. First, a BP neural network is used as the equalizer, an error function is redefined, and the back-propagation algorithm is used to train and adjust blind signal data deviation and to update network weight rules for adaptation to network settings with rapid time-variants. Second, a double-sigmoid BP neural network excitation function is constructed to improve poor multiple information processing and network performances resulting from social group densification. Third, self-adaptive variable step size for physical devices’ power is constructed to adjust the conflict between convergence rate and steady-state error caused by different powers with the increase of small-data-size communication. Finally, an output vector is made to be closest to an expected vector; thereby, blind signal detection rate of social groups’ communication using physical devices in the cyber environment can be improved. The experiments show that the communication blind signal detection method proposed in the paper improves signal detection precision, and reduces omission rate (below 2%). Besides, the method is characterized by minor error, removes deficiencies of traditional methods for blind signal extraction, and can effectively accomplish blind signal detection.

Weak Fault Signal Detection of Rotating Machinery Based on Multistable Stochastic Resonance and VMD‐AMD

For solving detection problems of multifrequency weak signals in noisy background, a novel weak signal detection method based on variational mode decomposition (VMD) and rescaling frequency‐shifted multistable stochastic resonance (RFMSR) with analytical mode decomposition (AMD) is proposed. In this method, different signal frequency bands are processed by rescaling subsampling compression to make each frequency band meet the conditions of stochastic resonance. Before the enhanced signal components are synthesized, they are processed to achieve the enhanced signal by means of AMD, leaving only the enhanced sections of the signal. The processed signal is decomposed into intrinsic mode functions (IMF) by VMD, in order to require the detection of weak multifrequency signals. The experimental analysis of the rolling bearing inner ring fault and gear fault diagnosis demonstrate that the proposed method can not only enhance signal amplitude, reduce false components, and improve the VMD algorithm’s accuracy, but also effectively detect weak multifrequency signals submerged by noise.

Signal Detection Methods Based on Less Matrix Inversion for Massive MIMO Systems

In this paper, we will represent several methods that can reduce the computational complexity to detect signals for Uplink Massive MIMO Systems. Then we will show the simulation performance of these methods and analyse them. Finally we will give improvement for better performance.