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Journal articles on the topic 'Signal detection'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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 (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 bloo

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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 (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 (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

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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 (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 cor

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Khudov, Hennadii, Serhii Yarosh, Oleksandr Kostyria, et al. "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) (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

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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 (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 propo

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Mao, Hongcun, Yuling Feng, Xiaoqian Wang, Chao Gao, Changhao Lin, and Zhihai Yao. "A New Chaotic Weak Signal Detection Method Based on a Simplified Fractional-Order Genesio–Tesi Chaotic System." Fractal and Fractional 9, no. 2 (2025): 74. https://doi.org/10.3390/fractalfract9020074.

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The detection of weak signals is a well-established application in chaos theory. This theory leverages the inherent robustness of chaotic systems, enabling them to resist noise and thus serve as effective tools for identifying weak signals. However, challenges remain in selecting appropriate chaotic systems and in their practical implementation—areas that are still under-explored. In this paper, we analyze a simplified fractional-order Genesio–Tesi chaotic system, which exhibits a unique chaos-divergence characteristic. Based on this characteristic, we propose a new detection method that uses

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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

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Kumar, Anoop, and Henna Khan. "Signal Detection and their Assessment in Pharmacovigilance." Open Pharmaceutical Sciences Journal 2, no. 1 (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 t

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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 (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 pr

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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 (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 (2005): B2.1.1—B2.1.10. http://dx.doi.org/10.1002/0471142719.mib0201s13.

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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 t

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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 (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 wavel

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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 (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 r

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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 (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,

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Безрук, Валерий Михайлович, та Станислав Андреевич Иваненко. "СРАВНИТЕЛЬНЫЙ АНАЛИЗ АЛГОРИТМОВ ОБНАРУЖЕНИЯ НЕИЗВЕСТНЫХ СИГНАЛОВ С УЧЁТОМ СОВОКУПНОСТИ ПОКАЗАТЕЛЕЙ КАЧЕСТВА". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 2 (8 жовтня 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 t

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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 (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 spectra

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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 (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 syst

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Buhaiov, M. V. "Energy Detector of Stochastic Signals in Noise Uncertainty." Visnyk NTUU KPI Seriia - Radiotekhnika Radioaparatobuduvannia, no. 94 (December 30, 2023): 32–40. https://doi.org/10.20535/radap.2023.94.32-40.

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Wide use of software-defined radio has led to a significant sophistication of electronic environment. This is mainly due to ability of generation signals of almost any shape. To detect signals with an unknown dynamic frequency-time structure, it is advisable to use advanced energy detector algorithms. The purpose of this article is to automate processes of stochastic signals detection and time parameters estimation under the conditions of unknown frequency-time structure of signals and noise power. The essence of proposed method is to detect and track temporal energy changes averaged over L sa

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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 dete

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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 (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 Even

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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 (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

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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 (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

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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 (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 desi

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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 (2012): 88–89. http://dx.doi.org/10.15373/22778179/apr2013/34.

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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 (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 detec

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Sarah, Sabah Mohammed, and K. Mahmood Al-Azawi Maher. "Performance comparison of some weak signal detection techniques." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 732–42. https://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 det

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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 (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 pres

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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 (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

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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 (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 predicte

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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 (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

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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 (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 eval

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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 (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 requir

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Chen, Bao Sheng. "Fault Signal Detection Model for Large-Scale Circuit Communication System." Applied Mechanics and Materials 651-653 (September 2014): 432–35. http://dx.doi.org/10.4028/www.scientific.net/amm.651-653.432.

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In the process of fault signal detection for large-scale circuit communication systems, with traditional methods to process detection, the fault detection method is more conservative. A fault signal detection for large-scale circuit communication system based on QRS wave group detection method is proposed. The signal to be measured is transformed appropriately in the time domain or frequency domain to strengthen or separate the QRS component, in order to suppress interference from various noise to signals, and the fault point of circuit communication system fault signal is identified, the filt

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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 (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 acousticall

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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 (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, a

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Shen, Zhiyuan, and Qianqian Wang. "A High-Precision Spectrum-Detection Algorithm Based on the Normalized Variance of Nonreconstruction Compression Sensing." Mathematical Problems in Engineering 2020 (March 19, 2020): 1–9. http://dx.doi.org/10.1155/2020/9867823.

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The traditional energy detection algorithm has been widely used in the field of signal detection, and a variety of improved algorithms have been derived. In the case of low signal-to-noise ratio, existing methods have shortcomings on achieving fast and accurate spectrum sensing that need to be resolved. This work proposes a normalized-variance-detection method based on compression sensing measurements of received signal. The discrete cosine transform sensing matrix is used to compress the signal, whose normalized variance is then calculated before being used as the testing variable for detecti

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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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K. Pujitha, J. Indu, B. Sasi Vardhan, P. Sandeep Kumar, and Mrs. G. Ramadevi. "Traffic Signal Violation Detection System." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2766–71. https://doi.org/10.32628/cseit2511141.

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Traffic signal violations are a major cause of accidents and traffic congestion. This project presents an automated Traffic Signal Violation Detection System using Deep Learning-based Object Detection. The system leverages SSD MobileNet V1, a pre-trained Convolutional Neural Network (CNN), to detect and classify traffic signals in real-time. Using the TensorFlow Object Detection API, the model identifies traffic lights and determines violations based on detected signals. The approach integrates image processing, real-time object detection, and violation recognition, providing an intelligent tr

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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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Yeh, Cheng-Yu, and Shaw-Hwa Hwang. "Efficient Detection Approach for DTMF Signal Detection." Applied Sciences 9, no. 3 (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 comp

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Cheng, Le, Yue Liu, Bingbing Zhang, Zhengliang Hu, Hongna Zhu, and Bin Luo. "Direction of Arrival Joint Prediction of Underwater Acoustic Communication Signals Using Faster R-CNN and Frequency–Azimuth Spectrum." Remote Sensing 16, no. 14 (2024): 2563. http://dx.doi.org/10.3390/rs16142563.

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Utilizing hydrophone arrays for detecting underwater acoustic communication (UWAC) signals leverages spatial information to enhance detection efficiency and expand the perceptual range. This study redefines the task of UWAC signal detection as an object detection problem within the frequency–azimuth (FRAZ) spectrum. Employing Faster R-CNN as a signal detector, the proposed method facilitates the joint prediction of UWAC signals, including estimates of the number of sources, modulation type, frequency band, and direction of arrival (DOA). The proposed method extracts precise frequency and DOA f

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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 sig

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Yang, Nachuan, Yongjun Zhao, Fuqiang Wang, and Jinyang Chen. "Using Phase-Sensitive Optical Time Domain Reflectometers to Develop an Alignment-Free End-to-End Multitarget Recognition Model." Electronics 12, no. 7 (2023): 1617. http://dx.doi.org/10.3390/electronics12071617.

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This pattern recognition method can effectively identify vibration signals collected by a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and improve the accuracy of alarms. An alignment-free end-to-end multi-vibration event detection method based on Φ-OTDR is proposed, effectively detecting different vibration events in different frequency bands. The pulse accumulation and pulse cancellers determine the location of vibration events. The local differential detection method demodulates the vibration event time-domain variation signals. After the extraction of the signal time-frequenc

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Liu, Yutong, Haiyang Tu, and Chen Lou. "Post-disaster Rescue Heartbeat Detection Signal Processing Algorithm Based on Laser Speckle Vibration Measurement Principle." Journal of Intelligence and Knowledge Engineering 2, no. 3 (2024): 54–59. https://doi.org/10.62517/jike.202404306.

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This paper proposes a heartbeat detection signal-processing algorithm based on the laser speckle vibration measurement principle, specifically designed for life-sign detection in post-disaster rescue operations. Laser speckle vibration measurement is a non-contact measurement technique that extracts vibration information of a target by illuminating its surface with a laser beam and detecting changes in the speckle pattern caused by minute surface vibrations. In this study, we use the laser speckle vibration measurement principle to acquire heartbeat signals from trapped individuals. To enhance

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Nguyen, Thien An, Xuan-Toan Dang, Oh-Soon Shin, and Jaejin Lee. "Combining the Viterbi Algorithm and Graph Neural Networks for Efficient MIMO Detection." Electronics 14, no. 9 (2025): 1698. https://doi.org/10.3390/electronics14091698.

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In the advancement of wireless communication, multiple-input, multiple-output (MIMO) detection has emerged as a promising technique to meet the high throughput requirements of 6G networks. Traditionally, MIMO detection relies on conventional algorithms, such as zero forcing and minimum mean square error, to mitigate interference and enhance the desired signal. Mathematically, these algorithms operate as linear transformations or functions of received signals. To further enhance MIMO detection performance, researchers have explored the use of nonlinear transformations and functions by leveragin

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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 (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

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Sorkin, Robert D. "Spreadsheet signal detection." Behavior Research Methods, Instruments, & Computers 31, no. 1 (1999): 46–54. http://dx.doi.org/10.3758/bf03207691.

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