Journal articles on the topic 'Signal detection'
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Gudiškis, Andrius. "HEART BEAT DETECTION IN NOISY ECG SIGNALS USING STATISTICAL ANALYSIS OF THE AUTOMATICALLY DETECTED ANNOTATIONS / ŠIRDIES DŪŽIŲ NUSTATYMAS IŠ IŠKRAIPYTŲ EKG SIGNALŲ ATLIEKANT AUTOMATIŠKAI APTIKTŲ ATSKAITŲ STATISTINĘ ANALIZĘ." Mokslas – Lietuvos ateitis 7, no. 3 (July 13, 2015): 300–303. http://dx.doi.org/10.3846/mla.2015.787.
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This paper proposes an algorithm to reduce the noise distortion influence in heartbeat annotation detection in electrocardiogram (ECG) signals. Boundary estimation module is based on energy detector. Heartbeat detection is usually performed by QRS detectors that are able to find QRS regions in a ECG signal that are a direct representation of a heartbeat. However, QRS performs as intended only in cases where ECG signals have high signal to noise ratio, when there are more noticeable signal distortion detectors accuracy decreases. Proposed algorithm uses additional data, taken from arterial blood pressure signal which was recorded in parallel to ECG signal, and uses it to support the QRS detection process in distorted signal areas. Proposed algorithm performs as well as classical QRS detectors in cases where signal to noise ratio is high, compared to the heartbeat annotations provided by experts. In signals with considerably lower signal to noise ratio proposed algorithm improved the detection accuracy to up to 6%. Širdies ritmas yra vienas svarbiausių ir daugiausia informacijos apie pacientų būklę teikiančių fiziologinių parametrų. Širdies ritmas nustatomas iš elektrokardiogramos (EKG), atliekant QRS regionų, kurie yra interpretuojami kaip širdies dūžio ãtskaitos, paiešką. QRS regionų aptikimas yra klasikinis uždavinys, nagrinėjamas jau keletą dešimtmečių, todėl širdies dūžių nustatymo iš EKG signalų metodų yra labai daug. Deja, šie metodai tikslūs ir patikimi tik esant dideliam signalo ir triukšmo santykiui. Kai EKG signalai labai iškraipomi, QRS aptiktuvai ne visada gali atskirti QRS regioną, o kartais jį randa ten, kur iš tikro jo būti neturėtų. Straipsnyje siūlomas algoritmas, kurį taikant sumažinama triukšmo įtaka nustatant iš EKG signalų QRS regionus. Tam naudojamas QRS aptiktuvas, kartu prognozuojantis širdies dūžio atskaitą. Remiamasi arterinio kraujo spaudimo signalo duomenimis, renkama atskaitų statistika ir atliekama jos analizė.
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Thompson, William Forde, and Max Coltheart. "The role of signal detection and amplification in the induction of emotion by music." Behavioral and Brain Sciences 31, no. 5 (October 2008): 597–98. http://dx.doi.org/10.1017/s0140525x08005529.
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AbstractWe propose that the six mechanisms identified by Juslin & Västfjäll (J&V) fall into two categories: signal detection and amplification. Signal detection mechanisms are unmediated and induce emotion by directly detecting emotive signals in music. Amplifiers act in conjunction with signal detection mechanisms. We also draw attention to theoretical and empirical challenges associated with the proposed mechanisms.
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Park, Do-Hyun, Min-Wook Jeon, Da-Min Shin, and Hyoung-Nam Kim. "LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function." Sensors 23, no. 20 (October 18, 2023): 8564. http://dx.doi.org/10.3390/s23208564.
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In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models.
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Egberts, Toine C. G. "Signal Detection." Drug Safety 30, no. 7 (2007): 607–9. http://dx.doi.org/10.2165/00002018-200730070-00006.
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Cheng, Yu-Chung Norman, and E. Mark Haacke. "Signal Detection." Current Protocols in Magnetic Resonance Imaging 00, no. 1 (March 2001): B2.1.1—B2.1.10. http://dx.doi.org/10.1002/0471142719.mib0201s00.
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Cheng, Yu-Chung Norman, and E. Mark Haacke. "Signal Detection." Current Protocols in Magnetic Resonance Imaging 13, no. 1 (April 2005): B2.1.1—B2.1.10. http://dx.doi.org/10.1002/0471142719.mib0201s13.
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Kumar, Anoop, and Henna Khan. "Signal Detection and their Assessment in Pharmacovigilance." Open Pharmaceutical Sciences Journal 2, no. 1 (December 17, 2015): 66–73. http://dx.doi.org/10.2174/1874844901502010066.
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Signal detection and its assessment is the most important aspect in pharmacovigilance which plays a key role in ensuring that patients receive safe drugs. For detection of adverse drug reactions, clinical trials usually provide limited information as they are conducted under strictly controlled conditions. Some of the adverse drug reactions can be detected only after long term use in larger population and in specific patient groups due to specific concomitant medications or disease. The detection of unknown and unexpected safety signals as early as possible from post marketing data is one of the major challenge of pharmacovigilance. The current method of detecting a signal is predominantly based on spontaneous reporting, which is mainly helpful in detecting type B adverse effects and unusual type A adverse effects. Other sources of signals detection are prescription event monitoring, case control surveillance and follow up studies. Signal assessment is mainly performed by using Upsala Monitoring scale & Naranjo scale of probability to analyze the cause and effect analysis. Signal detection and their assessment is very vital and complex process. Thus, the main objective of this review is to provide a summary of the most common methods of signal detection and their assessment used in pharmacovigilance to confirm the safety of a drug. Recent developments, challenges, & future needs have also been discussed.
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Liu, Shuai, Xiang Chen, Ying Li, and Xiaochun Cheng. "Micro-Distortion Detection of Lidar Scanning Signals Based on Geometric Analysis." Symmetry 11, no. 12 (December 3, 2019): 1471. http://dx.doi.org/10.3390/sym11121471.
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When detecting micro-distortion of lidar scanning signals, current hardwires and algorithms have low compatibility, resulting in slow detection speed, high energy consumption, and poor performance against interference. A geometric statistics-based micro-distortion detection technology for lidar scanning signals was proposed. The proposed method built the overall framework of the technology, used TCD1209DG (made by TOSHIBA, Tokyo, Japan) to implement a linear array CCD (charge-coupled device) module for photoelectric conversion, signal charge storage, and transfer. Chip FPGA was used as the core component of the signal processing module for signal preprocessing of TCD1209DG output. Signal transmission units were designed with chip C8051, FT232, and RS-485 to perform lossless signal transmission between the host and any slave. The signal distortion feature matching algorithm based on geometric statistics was adopted. Micro-distortion detection of lidar scanning signals was achieved by extracting, counting, and matching the distorted signals. The correction of distorted signals was implemented with the proposed method. Experimental results showed that the proposed method had faster detection speed, lower detection energy consumption, and stronger anti-interference ability, which effectively improved micro-distortion correction.
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Khudov, Hennadii, Serhii Yarosh, Oleksandr Kostyria, Oleksandr Oleksenko, Mykola Khomik, Andrii Zvonko, Bohdan Lisohorskyi, Petro Mynko, Serhii Sukonko, and Taras Kravets. "Improving a method for non-coherent processing of signals by a network of two small-sized radars for detecting a stealth unmanned aerial vehicle." Eastern-European Journal of Enterprise Technologies 1, no. 9 (127) (February 28, 2024): 6–13. http://dx.doi.org/10.15587/1729-4061.2024.298598.
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The object of this study is the process of detecting stealth unmanned aerial vehicles by a network of two small-sized radars with incoherent signal processing. The main hypothesis of the study assumed that combining two small-sized radars into a network could improve the quality of detection of stealth unmanned aerial vehicles with incoherent signal processing. The improved method for detecting a stealth unmanned aerial vehicle by a network of two small-sized radars with incoherent signal processing, unlike the known ones, provides for the following: – synchronous inspection of the airspace by small-sized radars; – sounding signal emission by each small-sized radar; – reception of echo signals from a stealth unmanned aerial vehicle by two small-sized radars; – coordinated filtering of incoming echo signals (separation of echo signals); – quadratic detection of signals at the outputs of matched filters; – summation of the detected signals at the outputs of the matched filters; – summation of the outputs of adders of two small-sized radars. The scheme of a stealth unmanned aerial vehicle detector is presented, optimal according to the Neumann-Pearson criterion, with incoherent signal processing. The quality of detection of a stealth unmanned aerial vehicle by a network of two small-sized radars with incoherent signal processing was evaluated. It was found that with incoherent processing, the gain in the value of the conditional probability of correct detection is on average from 19 % to 26 %, depending on the value of the signal-to-noise ratio. The gain in the value of the conditional probability of correct detection is greater at low values of the signal-to-noise ratio. At the same time, the gain in signal-to-noise value is more significant with coherent signal processing than with non-coherent signal processing by a network of two small-sized radars
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Wang, Liwei, Senxiang Lu, Xiaoyuan Liu, and Jinhai Liu. "Two-Stage Ultrasound Signal Recognition Method Based on Envelope and Local Similarity Features." Machines 10, no. 12 (November 23, 2022): 1111. http://dx.doi.org/10.3390/machines10121111.
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Accurate identification of ultrasonic signals can effectively improve the accuracy of a defect detection and inversion. Current methods, based on machine learning and deep learning have been able to classify signals with significant differences. However, the ultrasonic internal detection signal is interspersed with a large number of anomalous signals of an unknown origin and is affected by the time shift of echo features and noise interference, which leads to the low recognition accuracy of the ultrasonic internal detection signal, at this stage. To address the above problems, this paper proposes a two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES). In the first stage, a normal signal classification method, based on the envelope feature extraction and fusion is proposed to solve the problem of the low ultrasonic signal classification accuracy under the conditions of the echo feature time shift and noise interference. In the second stage, an abnormal signal detection method, based on the local similarity feature extraction and enhancement is proposed to solve the problem of detecting abnormal signals in ultrasound internal detection data. The experimental results show that the accuracy of the two-stage ultrasonic signal recognition method, based on the envelope and local similarity features (TS-ES) in this paper is 97.43%, and the abnormal signal detection accuracy and recall rate are as high as 99.7% and 97.81%.
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Cai, Ming Shan. "Weak Signal Detection Principle Based on Chaotic Duffing Oscillator and its Simulation Method." Advanced Materials Research 108-111 (May 2010): 834–37. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.834.
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Based on Matlab as the software platform, chaos detection principle and methods used for weak signals detection are studied. The model used for simulation is established, then the simulation results of weak periodic signal detection in strong noise atmosphere are given and the steps for detecting weak signals with chaos method are listed. Furthermore, the influence of sampling period on system’s performance is studied. Simulation results show that the chao detection approach proposed in this paper can detect the signal even if it is small to 10-10v, and even when it is in the environment with strong noise, small signal with magnitude of only 5×10-9 v can be found. Chaos method has strong capability for weak signal detection which lay important foundation for exploiting virtual instrument.
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Schiano, J. L., A. J. Blauch, and M. D. Ginsberg. "Optimization of NQR Pulse Parameters using Feedback Control." Zeitschrift für Naturforschung A 55, no. 1-2 (February 1, 2000): 67–73. http://dx.doi.org/10.1515/zna-2000-1-213.
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A new method for increasing the probability of detecting nuclear resonance signals is demon-strated experimentally. It is well known that the detection of signals with a low signal to noise ratio (SNR) results in missed detections of false alarms. In situations where the noise is correlated or where limited data is averaging, it may not be possible to achieve a desired SNR through averaging alone. We present an alternative approach in which a feedback algorithm automatically adjusts pulse parameters so that the SNR and probability of correct detection are increased. Experimental results are presented for the detection of 14N NQR signals.
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Bae, Hyeon, Youn-Tae Kim, Sungshin Kim, Sang-Hyuk Lee, and Bo-Hyeun Wang. "Fault Detection of Induction Motors Using Fourier and Wavelet Analysis." Journal of Advanced Computational Intelligence and Intelligent Informatics 8, no. 4 (July 20, 2004): 431–36. http://dx.doi.org/10.20965/jaciii.2004.p0431.
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The motor is the workhorse of industries. The issues of preventive and condition-based maintenance, online monitoring, system fault detection, diagnosis, and prognosis are of increasing importance. This paper introduces fault detection for induction motors. Stator currents are measured by current meters and stored by time domain. The time domain is not suitable for representing current signals, so the frequency domain is applied to display signals. The Fourier Transform is employed to convert signals. After signal conversion, signal features must be extracted by signal processing such as wavelet and spectrum analysis. Features are entered in a pattern classification model such as a neural network model, a polynomial neural network, or a fuzzy inference model. This paper describes fault detection results that use Fourier and wavelet analysis. This combined approach is very useful and powerful for detecting signal features.
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Guan, Zhanrong. "Weak signal detection method based on nonlinear differential equations." Journal of Computational Methods in Sciences and Engineering 24, no. 2 (May 10, 2024): 1207–21. http://dx.doi.org/10.3233/jcm-247329.
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With the rapid development of computer network technology, it is often necessary to collect weak signals to collect favorable information. The development of signal detection technology is ongoing; however, various issues arise during the detection process. These issues include low efficiency and a high signal noise threshold. However, many problems will be encountered in the process of detection. In order to solve these problems, the nonlinear chaos theory is introduced to detect signals, and the simulation experiments of weak pulse signals and weak partial discharge signals are carried out respectively. The experimental results showed that the detection effect was remarkable in the quasi periodic state, and it had a good detection effect for weak pulse signals. At a signal-to-noise ratio of -25 dB, double coupling system, two-way ring coupling system, and single ring coupling system displayed detection success rates exceeding 98%. Meanwhile, the detection success rate of the strong coupling system was only 12%. Even at a noise signal ratio as low as -40 dB, the dual coupling system still maintained a detection success rate above 80%. The simulation results of partial discharge signal detection showed that there was a high fluctuation only at 2 ms, and the rest was basically stable at about 0 V, indicating that the system had a strong suppression effect on Gaussian white noise. When comparing the simulation results of the detection of the new chaotic system and the double coupling system, it was found that the new chaotic system has a superior impact in detecting weakly attenuated partial discharge signals. Through analysis of the system’s dynamic behavior, the research confirms its rich dynamic characteristics and sheds light on the reasons for phase state mutation and missed detection. The noise system is utilized for comparing the performance of various systems, with the goal of enhancing the system’s detection capability.
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Sai Sandeep.k, Sai Sandeep k., and P. Vijay Kumar. "Acoustic Signal Based Automatic Vehicle Detection System." International Journal of Scientific Research 2, no. 4 (June 1, 2012): 88–89. http://dx.doi.org/10.15373/22778179/apr2013/34.
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Fedotov, Aleksandr A. "Method for detecting R-waves of an ECG signal based on wavelet decomposition." Izmeritel`naya Tekhnika, no. 5 (2021): 67–72. http://dx.doi.org/10.32446/0368-1025it.2021-5-67-72.
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Increasing the efficiency of cardiological diagnostics based on the analysis of human heart rate variability necessitates the development of accurate methods for detecting the R-waves of the electrocardiosignal (ECG signal). A technique for detecting R-waves of an ECG signal based on the wavelet multiresolution analysis (WMRA). The proposed technique for detecting R-waves includes sequential stages of digital processing of an ECG signal: WMRA; a set of nonlinear operators; adaptive algorithm for detecting signal peaks. A comparative analysis of the proposed technique with existing approaches to the detection of R-waves of the ECG signal has been carried out. To obtain quantitative characteristics of evaluating the efficiency of detecting R-waves, we used imitation modeling of an ECG signal containing noises and interferences of various intensity and nature of occurrence. The effectiveness of the considered approaches to the detection of R-waves of the ECG signal was investigated for clinical recordings of ECG signal. The absolute error of measuring the RR-interval durations for model signals with different noise levels is estimated. It is shown that the proposed method for detecting R-waves of an ECG signal based on WMRA is characterized by small errors in measuring the duration of RR-intervals, high rates of true detection and small errors of false detection and omission.
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Luo, Xu, Lihong Wang, Shufeng Cao, Qiuhan Xiao, Hongjuan Yang, and Jianguo Zhao. "Signal Processing Methods of Enhanced Magnetic Memory Testing." Processes 11, no. 2 (January 17, 2023): 302. http://dx.doi.org/10.3390/pr11020302.
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As a particular kind of detection technology under weak magnetization, metal magnetic memory testing is very likely to be affected by external factors in the detecting process, which may lead to incorrect results. In order to minimize the negative influence of interrupting signals and improve the detection accuracy, this paper adopted the enhanced metal magnetic memory testing method to preliminarily increase the signal-to-noise ratio (SNR) of the detection signal and then compares the denoising effects of wavelet threshold denoising method, empirical mode decomposition (EMD) denoising method, EMD-wavelet threshold denoising method, ensemble EMD (EEMD), complementary EEMD (CEEMD), variational mode decomposition (VMD), local mean decomposition (LMD) and empirical wavelet transform (EWT) on the detection signal and the gradient signal respectively. The results show that the enhanced metal magnetic memory testing method can significantly increase the SNR of the obtained signal and cannot improve the SNR of a gradient signal which is generated from the obtained signal. The different denoising methods can further boost the SNR and improve the detection accuracy of the obtained signal and the gradient signal. Among the eight signal processing methods, wavelet threshold, EMD and its improved methods are more applicable in the denoising of enhanced metal magnetic memory testing signals. The Wavelet threshold denoising, EMD-wavelet threshold denoising and EEMD denoising all have good denoising effects, and the denoising results to the same signal are analogous.
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Chen, Penghui, Liuyang Tian, Yujing Bai, and Jun Wang. "Rotating Target Detection Using Commercial 5G Signal." Applied Sciences 14, no. 10 (May 18, 2024): 4282. http://dx.doi.org/10.3390/app14104282.
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Passive radar detection emerges as a pivotal method for environmental perception and target detection within radar applications. Through leveraging its advantages, including minimal electromagnetic pollution and efficient spectrum utilization, passive radar methodologies have garnered increasing interest. In recent years, there has been an increasing selection of passive radar signal sources, and the emerging 5G has the characteristics of a high-frequency band, high bandwidth, and a large number of base stations, which give it significant advantages for use in passive radar. Therefore, in this paper, we introduce a passive radar target detection method based on 5G signals and design a rotating target speed measurement experiment. In the experiment, this paper validated the method of detecting rotating targets using 5G signals and evaluated the measurement accuracy, providing a research foundation for passive radar target detection using 5G signals and detecting rotating targets such as drone rotors.
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Безрук, Валерий Михайлович, and Станислав Андреевич Иваненко. "СРАВНИТЕЛЬНЫЙ АНАЛИЗ АЛГОРИТМОВ ОБНАРУЖЕНИЯ НЕИЗВЕСТНЫХ СИГНАЛОВ С УЧЁТОМ СОВОКУПНОСТИ ПОКАЗАТЕЛЕЙ КАЧЕСТВА." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 2 (October 8, 2018): 67–74. http://dx.doi.org/10.32620/reks.2018.2.07.
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The subject of this article is the problem of detecting unknown signals in conditions of high a priori uncertainty, which can occur during the determination of unoccupied frequency channels in cognitive networks. It should be noted that various sources of radio emissions work on the air, which in turn complicates the solution of the problem of detection, since it is impossible to say what kind of signal will be received. Most existing algorithms require information about the signals for their operation. In practice, it is not possible to have such data on all sources of radio emission due to their diversity. The goal of the article is to study non-classical signal detection algorithms in conditions of high a priori uncertainty, when there is information only about noise, and signals are unknown. The task: to conduct a comparative analysis of unknown signal detection algorithms based on a set of quality indicators and to determine the set of Pareto-optimal detection algorithms, as well as to identify the best algorithm for a set of quality indicators. The method of statistical modeling of detection algorithms on samples of real signals and noise is performed. As a result, we obtained estimates of speed of work and quality of signal detection for a number of different variants of unknown signal detection algorithms. Possible variants of implementation of the detectors were summarized in the table. These variants were formed taking into account the dimension of the DPF sample and the number of implementations on which the decision is made. A comparative analysis of different types of detection algorithms is carried out taking into account the set of performance indicators and the quality of signal detection. It should be noted that the values of quality indicators of detection of unknown signals and performance indicators of the algorithms are related and contradictory. Conclusions. A multicriteria selection of a subset of Pareto-optimal variants is performed. Using the conditional preference criterion, the only preferred variant of the algorithm for detecting unknown signals is selected from the Pareto subset. The results of the research can be used in automated radio monitoring in cognitive radio networks
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Lin, Meiyan, Xiaoxu Zhang, Ye Tian, and Yonghui Huang. "Multi-Signal Detection Framework: A Deep Learning Based Carrier Frequency and Bandwidth Estimation." Sensors 22, no. 10 (May 21, 2022): 3909. http://dx.doi.org/10.3390/s22103909.
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Multi-signal detection is of great significance in civil and military fields, such as cognitive radio (CR), spectrum monitoring, and signal reconnaissance, which refers to jointly detecting the presence of multiple signals in the observed frequency band, as well as estimating their carrier frequencies and bandwidths. In this work, a deep learning-based framework named SigdetNet is proposed, which takes the power spectrum as the network’s input to localize the spectral locations of the signals. In the proposed framework, Welch’s periodogram is applied to reduce the variance in the power spectral density (PSD), followed by logarithmic transformation for signal enhancement. In particular, an encoder-decoder network with the embedding pyramid pooling module is constructed, aiming to extract multi-scale features relevant to signal detection. The influence of the frequency resolution, network architecture, and loss function on the detection performance is investigated. Extensive simulations are carried out to demonstrate that the proposed multi-signal detection method can achieve better performance than the other benchmark schemes.
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Mohammed, Sarah Sabah, and Maher K. Mahmood Al-Azawi. "Performance comparison of some weak signal detection techniques." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (May 1, 2022): 732. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp732-742.
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Performance comparison of some weak signal detection techniques is introduced. This comparison is very necessary since different applications require different operating conditions such as signal to noise ratio SNR, bandwidth, coherency, processing time and complexity. Three methods for detecting weak signals are considered. These are based on chaos theory, wavelet transform, and stocastic resonance. A detection algorithm based on a rectangular region in phase space plane is suggested in chaos method. The stocastic resonance method is considered in this research, as it is used for signal detection in underwater at a certain frequency. Simulation results obtained from MATLAB programs verify the studied methods giving an estimation of probability of detection and probability of false alarm versus SNR.
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Shan, Shijie, Jianming Zheng, Kai Wang, Ting Chen, and Yuhua Shi. "Weak Fault Diagnosis Method of Rolling Bearings Based on Variational Mode Decomposition and a Double-Coupled Duffing Oscillator." Applied Sciences 13, no. 14 (July 23, 2023): 8505. http://dx.doi.org/10.3390/app13148505.
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Aiming at the problems of the low detection accuracy and difficult identification of the early weak fault signals of rolling bearings, this paper proposes a method for detecting the early weak fault signals of rolling bearings based on a double-coupled Duffing system and VMD. The influence rule of system initial value on the response characteristics of a double-coupled Duffing system is studied, and the basis for its determination is given. The frequency of the built-in power of the system is normalized, and a variance evaluation standard for the output value of the double-coupled Duffing system for weak fault signals detection is established. In order to solve the interference problem of fault monitoring signals, VMD is proposed to pre-process the fault monitoring signals. The weak fault signal detection method proposed in this paper is tested and verified by simulation signals and rolling bearing fault signals. The results show that the method proposed in this paper can detect the weak fault signal with the lowest signal-to-noise ratio reduced by 2.96 dB compared with the traditional Duffing detection system, and it can accurately detect the early weak fault signal of rolling bearings.
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Noguchi, Yoshihiro, Tomoya Tachi, and Hitomi Teramachi. "Subset Analysis for Screening Drug–Drug Interaction Signal Using Pharmacovigilance Database." Pharmaceutics 12, no. 8 (August 12, 2020): 762. http://dx.doi.org/10.3390/pharmaceutics12080762.
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Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug–drug interactions. Although there are several algorithms for detecting drug–drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug–drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; “hypothetical” true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden’s index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug–drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug–drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.
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Zhang, Cai Tian, and Yi Bo Zhang. "Detection of Network Intrusion Signal in Deep Camouflage Based on Chaotic Synchronization." Applied Mechanics and Materials 380-384 (August 2013): 2695–98. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.2695.
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For detecting the network intrusion signal in deep camouflage precisely and effectively, a new detection method based chaotic synchronization is proposed in this paper. The Gaussian mixture model of the network data combined with expectation maximization algorithm is established firstly for the afterwards detection, the chaotic synchronization concept is proposed to detect the intrusion signals. According to the simulation result, the new method which this paper proposed shows good performance of detection the intrusion signals. The detection ROC is plotted for the chaotic synchronization detection method and traditional ARMA method, and it shows that the detection performance of the chaotic synchronization algorithm is much better than the traditional ARMA detection method. It shows good application prospect of the new method in the network intrusion signal detection.
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Yeh, Cheng-Yu, and Shaw-Hwa Hwang. "Efficient Detection Approach for DTMF Signal Detection." Applied Sciences 9, no. 3 (January 27, 2019): 422. http://dx.doi.org/10.3390/app9030422.
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A novel tone detection approach, designated as the multi-frequency detecting (MFD) algorithm, is presented in this work as an alternative to conventional single point detection approaches but it is an efficient way to achieve the aim of further computational load reduction for a dual-tone multi-frequency (DTMF) signal detection. The idea is that an optimal phase search is performed over the frequency band of interest in each tone detection, and then the optimal frequency response of a detector is built accordingly. In this manner, a DTMF detection task is done following one-time detection computation. This proposal demonstrates an overall computational load reduction of 80.49% and 74.06% in comparison with a discrete Fourier transform (DFT) approach and the Goertzel algorithm, respectively. This detection complexity reduction is an advantage and an important issue for applying DTMF detection technique to embedded devices.
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Xu, Qifan, Sichang Zhang, Siyu Li, Zhe Xu, Shouqi Cao, and Meiling Wang. "Design and Analysis of Micro Signal Detection Circuit for Magnetic Field Detection Utilizing Coil Sensors." Applied Sciences 14, no. 9 (April 25, 2024): 3618. http://dx.doi.org/10.3390/app14093618.
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Eddy current inspection has been extensively employed in non-destructive testing of various conductive materials. The coil probe, as a mainstream sensor in the eddy current detection system, inevitably encounters interference from external signals while transmitting its own signal. Therefore, developing techniques to extract valuable signals from noisy ones is crucial for ensuring accurate detection. Carbon fiber composites not only possess significantly lower electrical conductivity compared to conventional metallic materials but also exhibit notable anisotropy. To address this issue, we designed an ‘8’ coil probe set where the excitation coil does not electromagnetically interfere with the detection coil. However, practical applications that require portability and miniaturization pose challenges when utilizing this coil probe set to identify carbon content or defects due to the typically weak output signal. To address this issue, this paper proposes a design that combines the ‘8’ structure of the planar coil probe with the principle of phase-locked amplification to create a dual-phase sensitive phase-locked amplification detection circuit. These specific design ideas were tested using a weak signal, which passed through the preamplifier, secondary amplifier, and band-pass filter comprising the target channel for signal amplification and noise filtering. The effective signal amplitude is proportional to the inverse phase difference between the direct current (DC) signal and inversely proportional to the amplitude of the signal. Finally, the DC signal was passed through an analog-to-digital converter (ADC). The analog-to-digital converter (A/D) is used to collect and calculate the DC signal, enabling the detection of weak electrical signals. Simulation experiments demonstrated that the signal detection circuit has an amplitude error below 0.2% and a phase error below 0.5%. The phase-locked amplification circuit designed in this paper can effectively extract the tiny impedance change signals of the planar coil sensor probe with high sensitivity and good robustness.
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Ding, Shaohu, Chenchen Yang, and Sen Zhang. "Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review." Sensors 23, no. 11 (May 23, 2023): 4987. http://dx.doi.org/10.3390/s23114987.
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Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.
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Winursito, A., F. Arifin, A. Nasuha, A. S. Priambodo, and Muslikhin. "Design of Robust Heart Abnormality Detection System based on Wavelet Denoising Algorithm." Journal of Physics: Conference Series 2111, no. 1 (November 1, 2021): 012048. http://dx.doi.org/10.1088/1742-6596/2111/1/012048.
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Abstract The technology that continues to be developed by many researchers today is an automatic heart attack detection system based on an Electrocardiogram (ECG) signal. Several other studies have been carried out to build an Internet of Things (IoT) based heart abnormality detection system. Based on the analysis of related studies that have been carried out previously, several researchers have developed an ECG signal-based heart abnormality detection system using clean ECG signal data. While the reality of the concept of an IoT-based detection system, the process of recording ECG signal data on the sensor, the process of sending data to the server, and the process of retrieving data from the server are vulnerable to noise exposure. ECG signal containing noise will greatly affect the accuracy of system detection. This paper proposes the development of a noise-resistant heart condition detection system using a wavelet denoising algorithm. The process of denoising ECG signals using the Wavelet algorithm is generally able to improve the accuracy of detecting noisy ECG signals. The most significant increase in accuracy is seen in the low SNR value. The Daubechies 4 (db4) denoising algorithm is the best-performing algorithm. The ECG signal classification method uses the Artificial Neural Network (ANN) algorithm. Detection system hardware is also designed in this research using the concept based on the Internet of Things.
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Sorkin, Robert D. "Spreadsheet signal detection." Behavior Research Methods, Instruments, & Computers 31, no. 1 (March 1999): 46–54. http://dx.doi.org/10.3758/bf03207691.
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Li, Xu Wen, Bi Wei Zhang, and Qiang Wu. "Study for Detection Algorithm of QRS Complex in ECG Signal." Advanced Materials Research 765-767 (September 2013): 2105–8. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.2105.
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In ECG signals accurate detection to the position of QRS complex is a key to automatic analysis and diagnosis system. And its premise is that effectively remove all kinds of noise interference in ECG signal. Here, a method of detecting QRS based on EMD and wavelet transform was presented which is aim to improve the anti-noise performance of the detection algorithm. It is combined EMD with the theory of singularity detecting based on wavelet transform modulus maxima method. It has the high detection accuracy and good precision that can give an effective way to the automatic analysis for ECG signal.
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Chang, Jaewon, Junil Ahn, Jeungmin Joo, and Dongweon Lee. "Development of Wideband Multi-Channel Receiver for Direction Finding of Communication Signals." Journal of the Korea Institute of Military Science and Technology 24, no. 5 (October 5, 2021): 527–36. http://dx.doi.org/10.9766/kimst.2021.24.5.527.
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In wireless environments, wideband receivers are used in a communication intelligent system to detect unknown signals and obtain azimuth information. To design a wideband receiver that performs multiple signal detection and direction finding simultaneously, it is necessary to consider a reception structure composed of multiple channels. In this paper, we propose a wideband multi-channel receiver for direction finding of unknown wideband communication signals including frequency hopping signals. A signal processing method for detecting received signals and estimating azimuth information is presented, and components of the manufactured wideband receiver are described. In addition, test results of the signal detection performance by mounting the proposed wideband multi-channel receiver on the flight system are included.
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Kumar, Pradeep, Guo-Liang Shih, Cheng-Kai Yao, Stotaw Talbachew Hayle, Yibeltal Chanie Manie, and Peng-Chun Peng. "Intelligent Vibration Monitoring System for Smart Industry Utilizing Optical Fiber Sensor Combined with Machine Learning." Electronics 12, no. 20 (October 17, 2023): 4302. http://dx.doi.org/10.3390/electronics12204302.
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In this paper, we proposed and experimentally demonstrated the association of a fiber Bragg Grating (FBG) sensing system with You Only Look Once V7 (YOLO V7) to identify the vibration signal of a faulty machine. In the experiment, the YOLO V7 network architecture consists of a backbone, three detection heads (Headx3), a path aggregation network (PAN), and a feature pyramid network (FPN). The proposed architecture has an FBG sensor and the FBG interrogator employed for collecting sensing vibration signals or vibration data when degradation or fault occurs. An FBG interrogator collects vibration data independently, and then the YOLO V7 object detection algorithm is the recognition architecture of the vibration pattern of the signal. Thus, the proposed vibration recognition or detection is an assurance for detecting vibration signals that can support monitoring the machine’s health. Moreover, this research is promising for ensuring a high accuracy detection of faulty signals rate in industrial equipment monitoring and offers a robust system, resulting in remarkable accuracy with an overall model accuracy of 99.7%. The result shows that the model can identify the faulty signal more accurately and effectively detect the faulty vibration signal using the detection algorithm.
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Vovkodav, Nataliya G., and Leonard N. Shlepakov. "Optimal Signal Detection and Tracking of Detected Signals." Journal of Automation and Information Sciences 32, no. 11 (2000): 65–71. http://dx.doi.org/10.1615/jautomatinfscien.v32.i11.90.
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Poor, H. Vincent. "Detection of broadband signals in signal‐dependent noise." Journal of the Acoustical Society of America 87, no. 3 (March 1990): 1227–30. http://dx.doi.org/10.1121/1.398797.
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Tadaion, A. A., M. Derakhtian, S. Gazor, M. M. Nayebi, and M. R. Aref. "Signal Activity Detection of Phase-Shift Keying Signals." IEEE Transactions on Communications 54, no. 6 (June 2006): 1143. http://dx.doi.org/10.1109/tcomm.2006.876884.
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Tadaion, A. A., M. Derakhtian, S. Gazor, M. M. Nayebi, and M. R. Aref. "Signal activity detection of phase-shift keying signals." IEEE Transactions on Communications 54, no. 8 (August 2006): 1439–45. http://dx.doi.org/10.1109/tcomm.2006.878830.
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Fardipour Raki, Gholamreza, Maryam Ghahremani Gol, Mohammad Sahraei, and Mohsen Khakzad. "SiPM and PMT driving, signals count, and peak detection circuits, suitable for particle detection." Journal of Instrumentation 17, no. 09 (September 1, 2022): T09011. http://dx.doi.org/10.1088/1748-0221/17/09/t09011.
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Abstract The signals received from the optical receivers like SiPM and PMT due to the collision of energetic particles with the scintillators attached to these optical receivers are weak and fast. Optimizing signals is necessary to measure the number of signals and their peak height with electronic circuits. This text presents an example of SiPM's driver circuit, signal counting, and peak measurement. Also, the electronic circuits necessary to optimize the signals, including amplification, removing background noise, converting the signal to digital, and increasing the duration of the signal, are presented in this text. In the end, we provide two tests to confirm the correct operation of the circuits. Such a system has several advantages. This set has a small volume and is portable. Its operating voltage is 12 volts, with a current of about 0.3 amps; as a result, it is easily possible to use this set in any experiment. In addition, the cost of building such a system is much lower than providing similar ready-made designs. The most important achievement here is to convert the standard signal taken from the detector into an almost ideal optimized signal for signal counting and peak measurement. Therefore, it seems that using all or part of these circuits can be helpful for researchers. This text presents a particular method for signal optimization and provides the reader with a coherent and complete process of building and testing circuits. If the reader is familiar with the basics of electronics and detectors, they can reconstruct the circuits without any problems. Therefore, parts of this text may have an educational and review form.
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S. Mohammed, Sarah, and Maher K. Mahmood. "WEAK SIGNAL DETECTION BASED ON MULTIPLE AUTO-CORRELATION TECHNIQUES." Journal of Engineering and Sustainable Development 25, Special (September 20, 2021): 1–56. http://dx.doi.org/10.31272/jeasd.conf.2.1.8.
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This study presents the performance of the auto-correlation methods for detecting weak signals, where the signal level is much less than the noise level. Double and triple auto-correlation techniques are used to improve the detection performance compared with the single autocorrelation. Simulation results obtained by MATLAB programs show that the multiple correlation techniques outperform the single correlation in terms of probability of detection and probability of false alarm versus signal to noise ratio SNR.
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Tulyakova, N. O., and O. M. Trofymchuk. "Modified algorithms for signal nonlinear trend detection." Radiotekhnika, no. 206 (September 24, 2021): 137–51. http://dx.doi.org/10.30837/rt.2021.3.206.13.
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There is a problem of nonlinear (abrupt) signal trend detection in many digital signals processing practical applications. In particular, in the field of biomedical signals processing, the actual task is the elimination of abrupt signal baseline distortions caused by the patient's movements. For processing such signals containing edges and other discontinues, linear filtering based on discrete Fourier or cosine transforms leads to significant smoothing of a signal. Median type algorithms related to nonlinear stable (robust) filters are successfully applied for filtering such signals, in particular, high efficiency is provided by median hybrid filters with finite impulse response (FIR). The article considers simple algorithms of the class of FIR-median hybrid filters used for signal nonlinear trend detection. It is proposed to modify these algorithms by replacing the operation of finding the median of the data in the sliding filter window with the calculation of their myriad, as well as adding weights (number of duplications) to certain window elements. Statistical estimates of filter efficiency according to the mean square error (MSE) criterion for test signals like “step” and “ramp” edges, and triangular peak and parabola have been obtained. The high efficiency of the investigated nonlinear filters for the listed test signals types and the improvements achieved as a result of the proposed filter modifications are shown based on the analysis of the filter output signals and statistical estimates of their quality. Some examples of processing biomedical signals of electroencephalograms which illustrate good quality of noise suppression and signal abrupt changes preservation, and motion artifacts removal without large signal distortions are given.
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Olesiński, Adam, and Zbigniew Piotrowski. "A Radio Frequency Region-of-Interest Convolutional Neural Network for Wideband Spectrum Sensing." Sensors 23, no. 14 (July 18, 2023): 6480. http://dx.doi.org/10.3390/s23146480.
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Wideband spectrum sensing plays a crucial role in various wireless communication applications. Traditional methods, such as energy detection with thresholding, have limitations like detecting signals with low signal-to-noise ratio (SNR). This article proposes a novel deep learning-based approach for RF signal detection in the wideband spectrum. The objective is to accurately estimate the noise distribution in a wideband radio spectrogram and improve the detection performance by substracting it. The proposed method utilizes convolutional neural networks to analyze radio spectrograms. Model evaluation demonstrates that the RFROI-CNN approach outperforms the traditional energy detection with thresholding method by achieving significantly better detection results, even up to 6 dB, and expanding the capabilities of wideband spectrum sensing systems. The proposed approach, with its precise estimation of noise distribution and consideration of neighboring signal power values, proves to be a promising solution for RF signal detection.
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Wu, Yan Jun, Gang Fu, and Yu Ming Zhu. "LFM Signal Detection Method Based on Fractional Fourier Transform." Advanced Materials Research 989-994 (July 2014): 4001–4. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.4001.
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As a generalization of Fourier transform, the fractional Fourier Transform (FRFT) contains simultaneity the time-frequency information of the signal, and it is considered a new tool for time-frequency analysis. This paper discusses some steps of FRFT in signal detection based on the decomposition of FRFT. With the help of the property that a LFM signal can produce a strong impulse in the FRFT domain, the signal can be detected conveniently. Experimental analysis shows that the proposed method is effective in detecting LFM signals.
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Yan, Zheng Guo, and Juan Su. "Through-Casing Resistivity Logging Signal Acquisition and Processing Techniques." Advanced Materials Research 403-408 (November 2011): 2659–62. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.2659.
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Weak signal detection is the key technique in developing through-casing resistivity logging tool. In this paper, ultra-low-noise preamplifier, oversampling method, sampling integration and sampling average method, digital phase-sensitive detection technique are applied in detecting logging signals and 30nV is achieved. The indoor calibration test and field experiment of through-casing resistivity logging model machine with those weak signal detection techniques were carried out. The result showed that the measurement range of formation resistivity is 0~200 Ω.m.
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Elvers, Greg C., and Robert D. Sorkin. "Detection and Recognition of Multiple Visual Signals in Noise." Proceedings of the Human Factors Society Annual Meeting 33, no. 20 (October 1989): 1383–87. http://dx.doi.org/10.1177/154193128903302004.
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This experiment tested a detection theory model of visual signal detection and recognition. The task employed a visual display consisting of analog gauges arranged in a horizontal line. The signals to be detected and identified were three unique patterns of gauge values embedded in noise. After viewing the display the observers either reported that any of the signals had occurred (1-of-m signal detection) or specified which of the signals (if any) had occurred (1-of-m signal recognition-detection). The results indicated that performance on 1-of-m recognition and detection tasks can be predicted from performance on the component single-signal detection tasks.
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Zha, Xiong, Hua Peng, Xin Qin, Guang Li, and Sihan Yang. "A Deep Learning Framework for Signal Detection and Modulation Classification." Sensors 19, no. 18 (September 19, 2019): 4042. http://dx.doi.org/10.3390/s19184042.
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Deep learning (DL) is a powerful technique which has achieved great success in many applications. However, its usage in communication systems has not been well explored. This paper investigates algorithms for multi-signals detection and modulation classification, which are significant in many communication systems. In this work, a DL framework for multi-signals detection and modulation recognition is proposed. Compared to some existing methods, the signal modulation format, center frequency, and start-stop time can be obtained from the proposed scheme. Furthermore, two types of networks are built: (1) Single shot multibox detector (SSD) networks for signal detection and (2) multi-inputs convolutional neural networks (CNNs) for modulation recognition. Additionally, the importance of signal representation to different tasks is investigated. Experimental results demonstrate that the DL framework is capable of detecting and recognizing signals. And compared to the traditional methods and other deep network techniques, the current built DL framework can achieve better performance.
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Gu, Han-Qing, Xia-Xia Liu, Lu Xu, Yi-Jia Zhang, and Zhe-Ming Lu. "DSSS Signal Detection Based on CNN." Sensors 23, no. 15 (July 26, 2023): 6691. http://dx.doi.org/10.3390/s23156691.
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With the wide application of direct sequence spread spectrum (DSSS) signals, the comprehensive performance of DSSS communication systems has been continuously improved, making the electronic reconnaissance link in communication countermeasures more difficult. Electronic reconnaissance technology, as the fundamental means of modern electronic warfare, mainly includes signal detection, recognition, and parameter estimation. At present, research on DSSS detection algorithms is mostly based on the correlation characteristics of DSSS signals, and autocorrelation algorithm is the most mature and widely used method in practical engineering. With the continuous development of deep learning, deep-learning-based methods have gradually been introduced to replace traditional algorithms in the field of signal processing. This paper proposes a spread spectrum signal detection method based on convolutional neural network (CNN). Through experimental analysis, the detection performance of the CNN model proposed in this paper on DSSS signals in various situations has been compared and analyzed with traditional autocorrelation detection methods for different signal-to-noise ratios. The experiments verified the estimation performance of the model in this paper under different signal-to-noise ratios, different spreading code lengths, different spreading code types, and different modulation methods and compared it with the autocorrelation detection algorithm. It was found that the detection performance of the model in this paper was higher than that of the autocorrelation detection method, and the overall performance was improved by 4 dB.
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Swanborough, Huw, Matthias Staib, and Sascha Frühholz. "Neurocognitive dynamics of near-threshold voice signal detection and affective voice evaluation." Science Advances 6, no. 50 (December 2020): eabb3884. http://dx.doi.org/10.1126/sciadv.abb3884.
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Communication and voice signal detection in noisy environments are universal tasks for many species. The fundamental problem of detecting voice signals in noise (VIN) is underinvestigated especially in its temporal dynamic properties. We investigated VIN as a dynamic signal-to-noise ratio (SNR) problem to determine the neurocognitive dynamics of subthreshold evidence accrual and near-threshold voice signal detection. Experiment 1 showed that dynamic VIN, including a varying SNR and subthreshold sensory evidence accrual, is superior to similar conditions with nondynamic SNRs or with acoustically matched sounds. Furthermore, voice signals with affective meaning have a detection advantage during VIN. Experiment 2 demonstrated that VIN is driven by an effective neural integration in an auditory cortical-limbic network at and beyond the near-threshold detection point, which is preceded by activity in subcortical auditory nuclei. This demonstrates the superior recognition advantage of communication signals in dynamic noise contexts, especially when carrying socio-affective meaning.
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Ko, Hoon, Kwangcheol Rim, and Isabel Praça. "Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System." Sensors 21, no. 12 (June 21, 2021): 4237. http://dx.doi.org/10.3390/s21124237.
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The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).
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Zhu, Yu Tian, Hao Wu, and Zhao Liu. "Development of Online Oil Control Valve Detection System." Applied Mechanics and Materials 380-384 (August 2013): 833–36. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.833.
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An online detecting system is developed to check the function of oil control valve. This system consists of hydraulic detecting platform, signal collecting, signal processing and upper computer. It uses a domestic controller as the main control unit and the control program is developed. The signal conditioning board and the software of upper computer within LabVIEW environment are developed. By using this system, the signals of all sensors can be collected, conditioned, displayed and recorded quickly. The application shows that this system has been proved with the characteristics of high speed, high automation and detection precision.
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TRIPATHY, R. K., MARIO R. ARRIETA PATERNINA, and P. PATTANAIK. "A NEW METHOD FOR AUTOMATED DETECTION OF DIABETES FROM HEART RATE SIGNAL." Journal of Mechanics in Medicine and Biology 17, no. 07 (November 2017): 1740001. http://dx.doi.org/10.1142/s0219519417400012.
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Diabetes Mellitus (DM) is a chronic disease and it is characterized based on the increase in the sugar level in the blood. The other diseases such as the cardiomyopathy, neuropathy and retinopathy may occur due to the DM pathology. The RR-time series or heart rate (HR) signal quantifies the beat-to-beat variations in the electrocardiogram (ECG) and it has been widely used for the detection of various cardiac diseases. Detection of DM based on the features of HR signal is a challenging problem. This paper copes with a new method for the detection of Diabetes Mellitus (DM) based on the features extracted from the HR signal. The Singular Spectrum Analysis (SSA) of HR signal and the Kernel Sparse Representation Classifier (KSRC) are the mathematical foundations used to achieve the detection. SSA is used to decompose the HR signal into sub-signals, and diagnostic features such as the maximum value of each sub-signal and eigenvalues are evaluated. Then, the KSRC uses the proposed diagnostic features as inputs for detecting diabetes. The experimental results reveal that the proposal attains the accuracy, sensitivity, and specificity values of 92.18%, 93.75% and 90.62%, respectively, employing the KSRC and the hold-out cross-validation approach. The method is compared with existing approaches for detecting diabetes from HR signal.
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Su, Liyun, Wanlin Zhu, Fenglan Li, and Chunquan Pan. "Weak Sinusoidal Signal Detection with CSI Model in Chaotic Interference." Journal of Sensors 2023 (December 14, 2023): 1–14. http://dx.doi.org/10.1155/2023/5569714.
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In small target detection under strong sea clutter or impact signal detection under machinery fault diagnosis, a weak sinusoidal signal with random amplitude is often contaminated by heavier chaotic noise, and the target information is difficult to detect. Traditional solutions, such as neural networks or stochastic resonance, can not effectively extract heteroscedasticity of data, which leads to weak signals not being detected. To overcome these limitations and improve the detection efficiency, an empirical likelihood ratio statistical method for detecting weak sinusoidal signals with random amplitude under strong chaotic interference is proposed. First, based on the reconstruction in the phase space of the 1-D observed time series signal with embedding dimension and time delay, the presented method obtains a multivariate special temporal series as an input. Subsequently, the chaotic single index (CSI) statistical model is established for single-step prediction, and it can be estimated by the nonparametric locally linear algorithm for minimizing the mean squares error. Finally, the empirical likelihood ratio statistical method is applied to detect weak sinusoidal signals with random amplitude. Simulated data and real data experiment results show that the proposed CSI model can better capture the weak target signal and detect effectively weak target signal under the chaotic interference.