Autocorrelation Algorithm Research Articles

An ultrafast optical sensor for simultaneous detection of NH3 temperature and concentration based on ultraviolet absorption spectral redshift combined with spectral reconstruction

As a renewable and carbon-free fuel with high energy density, ammonia (NH3) is a promising alternative to fossil fuels. Real-time detection of its temperature and concentration is significant in improving NH3 combustion efficiency. In this paper, we report an optical sensor, which enables simultaneous measurement of temperature and concentration of NH3. The key idea is to acquire the temperature of NH3 through absorption spectral redshift, and determine its concentration by building three-dimensional field map combined with spectral reconstruction. Firstly, we explain the generation mechanism of temperature-induced redshift in ultraviolet differential absorption spectrum based on energy level distribution theory. The relationship between spectral redshift and temperature is then established using spectral autocorrelation algorithm. Secondly, a spectral reconstruction method based on absorbed intensity mapping wavelength is proposed to ensure real-time detection of NH3. Finally, a three-dimensional field map between temperature, reconstruction slope, and concentration is constructed to achieve the detection. Results indicated that the mean relative error in temperature measurement was 1.04% and the mean absolute error in concentration detection was 0.26mg/m3 in air. Additionally, the NH3 sensor enabled long-term detection with a detection limit of 15.50μg/m2, demonstrating its potential to broaden the application of NH3 as a fuel.

Enhancing Road Safety: Fast and Accurate Noncontact Driver HRV Detection Based on Huber-Kalman and Autocorrelation Algorithms.

Enhancing road safety by monitoring a driver's physical condition is critical in both conventional and autonomous driving contexts. Our research focuses on a wireless intelligent sensor system that utilizes millimeter-wave (mmWave) radar to monitor heart rate variability (HRV) in drivers. By assessing HRV, the system can detect early signs of drowsiness and sudden medical emergencies, such as heart attacks, thereby preventing accidents. This is particularly vital for fully self-driving (FSD) systems, as it ensures control is not transferred to an impaired driver. The proposed system employs a 60 GHz frequency-modulated continuous wave (FMCW) radar placed behind the driver's seat. This article mainly describes how advanced signal processing methods, including the Huber-Kalman filtering algorithm, are applied to mitigate the impact of respiration on heart rate detection. Additionally, the autocorrelation algorithm enables fast detection of vital signs. Intensive experiments demonstrate the system's effectiveness in accurately monitoring HRV, highlighting its potential to enhance safety and reliability in both traditional and autonomous driving environments.

Performance improvement of a distributed temperature sensor with kilometer length and centimeter spatial resolution based on polarization-sensitive OFDR

A method to improve the performance of distributed temperature sensors with kilometer sensing distance and centimeter spatial resolution is proposed based on polarization-sensitive optical frequency domain reflectometry (OFDR). This approach records the temperature change along polarization maintaining fiber (PMF) from the Rayleigh backscattering (RBS) spectral difference between two orthogonal polarization axes using the distributed autocorrelation algorithm, where PMF is used as the sensing fiber. In addition, a technique to calibrate population birefringence and local birefringence, and a method to calibrate the initial birefringence inhomogeneity of PMF are proposed to enhance the performance of the sensor. The birefringence of PMF is used for temperature sensing, which is distinct from the traditional cross-correlation method of single-mode optical fiber temperature sensing, providing us with an innovative scheme to solve real-world engineering problems. The final sensing performance is achieved, with a sensing distance of 1.5 km, a spatial resolution of 5 cm, and a temperature measurement uncertainty of ±0.2 °C, subject to environmental noise and system random noise.

Automated ultrasonic testing for near-surface flaws in CFRP

ABSTRACT In ultrasonic testing of Carbon Fiber Reinforced Polymers (CFRP), signals of near-surface flaws are often submerged in interface signals, resulting in blind spots for defect detection. To address this issue, this paper presents an autocorrelation imaging algorithm that combines Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with l 0 -norm sparse representation. Firstly, the ultrasonic signal can be modelled as a convolution process. By using MOMEDA, the sequence of reflected pulses in the ultrasonic signal is obtained. Then, the l 0 -norm sparse representation is utilised to enhance the temporal resolution of the sequence, successfully separating near-surface defect signals from interface signals. Finally, the processed data is input into the autocorrelation algorithm, achieving automated imaging of near-surface flaws. Simulation results demonstrate the efficacy of the algorithm in separating overlapping signals. Finally, experimental validation was conducted on flaws at three different depths. The algorithm presented in this paper is capable of identifying all near-surface flaws, with a defect size error rate consistently below 5%.

Spatial and temporal dynamics of urban heat environment at the township scale: A case study in Jinan city, China.

The prolonged dependence on industrial development has accentuated the cumulative effects of pollutants. Simultaneously, influenced by land construction activities and green space depletion, the Urban Heat Island (UHI) effect in cities has intensified year by year, jeopardizing the foundation of sustainable urban development. Prudent urban spatial planning holds the potential to robustly ameliorate the persistent deterioration of the UHI phenomenon. This study selects Jinan City as a case study and employs spatial autocorrelation and spatial regression algorithms to explore the spatiotemporal evolution of urban-rural patterns at the township scale. The aim is to identify key factors driving the spatiotemporal differentiation of Land Surface Temperature (LST) from 2013 to 2022. The research reveals a trend of initially rising and subsequently falling LST in various townships, with low-temperature concentration areas in the southern mountainous region and the northern plain area. The "West-Central-East" main urban axis and the southeast Laiwu District exhibit high-temperature zones. Significant influences on LST are attributed to pollution levels, topographical factors, urbanization levels, and urban greenness. The global Moran's Index for LST exceeds 0.7, indicating a strong positive spatial correlation. Cluster analysis results indicate High-High (HH) clustering in the central Shizhong District and Low-Low (LL) clustering in the northern Shanghe County. Multiscale Geographically Weighted Regression (MGWR) outperforms Geographically Weighted Regression (GWR) and Ordinary Linear Regression (OLR), providing a more accurate reflection of the regression relationships between variables. By investigating the spatiotemporal evolution of LST and its driving factors at the township scale, this study contributes insights for future urban planning and sustainable development.

Finding Multiply Lensed and Binary Quasars in the DESI Legacy Imaging Surveys

The time delay between multiple images of strongly lensed quasars is a powerful tool for measuring the Hubble constant (H 0). To achieve H 0 measurements with higher precision and accuracy using the time delay, it is crucial to expand the sample of lensed quasars. We conduct a search for strongly lensed quasars in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys. The DESI Legacy Surveys comprise 19,000 deg2 of the extragalactic sky observed in three optical bands (g, r, and z), making it well suited for the discovery of new strongly lensed quasars. We apply an autocorrelation algorithm to ∼5 million objects classified as quasars in the DESI Quasar Sample. These systems are visually inspected and ranked. Here, we present 436 new multiply lensed and binary quasar candidates, 65 of which have redshifts from Sloan Digital Sky Survey Data Release 16. We provide redshifts for an additional 18 candidates from the SuperNova Integral Field Spectrograph.

Novel developments of refractive power measurement techniques in the automotive world

Refractive power measurements serve as the primary quality standard in the automotive glazing industry. In the light of autonomous driving new optical metrics are becoming more and more popular for specifying optical quality requirements for the windshield. Nevertheless, the link between those quantities and the refractive power needs to be established in order to ensure a holistic requirement profile for the windshield. As a consequence, traceable high-resolution refractive power measurements are still required for the glass quality assessment. Standard measurement systems using Moiré patterns for refractive power monitoring in the automotive industry are highly resolution limited, wherefore they are insufficient for evaluating the camera window area. Consequently, there is a need for more sophisticated refractive power measurement systems that provide a higher spatial resolution. In addition, a calibration procedure has to be developed in order to guarantee for the comparability of the measurement results. For increasing the resolution, a measurement setup based on an auto-correlation algorithm is tested in this paper. In order to benchmark the proposed high-resolution refractive power measurement technique, a novel laser-based setup has been realized in the Volkswagen Laser Laboratory as a reference method tuned for high-accuracy measurements. Furthermore, a calibration procedure is established by using a single reference lens with a nominal refractive power of . For the calibration of the entire measurement range of the system, the lens is tilted by an inclination angle orthogonal to the optical axis. The effective refractive power is then given by the Kerkhof model. By adopting the measurement and calibration procedure presented in this paper, glass suppliers in the automotive industry will be able to detect relevant manufacturing defects within the camera window area more accurately paving the way for a holistic quality assurance of the windshield for future advanced driver-assistance system (ADAS) functionalities. Concurrently, the traceability of the measurement results is ensured by establishing a calibration chain based on a single reference lens, which is traced back to international standards.

A joint algorithm based on photon heterodyne detection

The photonic heterodyne detection system, which has both the anti-interference advantage of the heterodyne detection and the detection sensitivity at the photon level, is widely used in the detection of long-range non-cooperative targets. In this paper, a joint algorithm is proposed to improve the signal-to-noise ratio (SNR) of the system for the problem of low SNR in remote target detection. Firstly, the photon arrival sequences with initial phases in the range of (-π/4, π/4) are coherently accumulated, and then the autocorrelation algorithm is used to obtain the final power spectral density (PSD). After being processed by the joint algorithm, the differential frequency amplitude of the experimental signal is finally improved by a factor of 3.14 and the SNR is improved by a factor of 2.72.

SAC-ConvLSTM: A novel spatio-temporal deep learning-based approach for a short term power load forecasting

The short-term spatiotemporal load forecasting of power distribution networks has been extensively studied in light of high importance in distribution system control and consumption management. Load forecasting is dependent on temporal and spatial parameters e.g., temperature, rainfall, and land use. Additionally, load time series have spatial autocorrelation in cities. Deep learning has been recently demonstrated to be effective and efficient in time-series and load forecasting in power distribution networks. The present study proposes a new algorithm based on Spatial Auto Correlation and Convolutional Long Short-Term Memory (SAC-ConvLSTM) algorithm via spatiotemporal power load autocorrelation modeling and deep learning. Load time-series signals in a power distribution network are decomposed into sub-signals using the discrete wavelet transform (DWT). Then, the spatio-temporal autocorrelation of sub-signals is calculated, alleviating prediction model complexity using spatial statistics. The areas with significant positive/negative/no significant autocorrelation in each time-series sub-signal are separately used as the input of the SAC-ConvLSTM algorithm. Finally, the sub-signals are reconstructed into the load signals. This algorithm models the spatial relationships in time-series at different time intervals for power consumption forecasting. The proposed methodology was found to outperform the support vector machine (SVM), Long Short Term Memory (LSTM), Convolutional LSTM (ConvLSTM), Convolutional Gated Recurrent Unit (ConvGRU), Planar Flow-Based Variational Auto-Encoder (PFVAE) and Federated Averaging (FedAVG) algorithms in short-term power load forecasting for short term forecasting of up to two weeks, based on the evaluation metrics of root-mean-square error (RMSE) and mean absolute error (MAE), producing values of 11.08% and 7.02% respectively.

DSSS Signal Detection Based on CNN.

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.

Shear-Wave Particle-Velocity Estimation and Enhancement Using a Multi-Resolution Convolutional Neural Network.

Tissue mechanical properties are valuable markers for tissue characterization, aiding in the detection and staging of pathologies. Shear wave elastography (SWE) offers a quantitative assessment of tissue mechanical characteristics based on the SW propagation profile, which is derived from the SW particle motion. Improving the signal-to-noise ratio (SNR) of the SW particle motion would directly enhance the accuracy of the material property estimates such as elasticity or viscosity. In this paper, we present a 3-D multi-resolution convolutional neural network (MRCNN) to perform improved estimation of the SW particle velocity Vz. Additionally, we propose a novel approach to generate training data from real acquisitions, providing high SNR ground truth target data, one-to-one paired to inputs that are corrupted with real-world noise and disturbances. By testing the network on in vitro data acquired from a commercial breast elastography phantom, we show that the MRCNN outperforms Loupas' autocorrelation algorithm with an improved SNR of 4.47dB for the Vz signals, a two-fold decrease in the standard deviation of the downstream elasticity estimates, and a two-fold increase in the contrast-to-noise ratio of the elasticity maps. The generalizability of the network was further demonstrated with a set of ex vivo porcine liver data. The proposed MRCNN outperforms the standard autocorrelation method, in particular in low SNR regimes.

Spatially resolved transcriptomic analysis of the germinating barley grain.

Seeds are a vital source of calories for humans and a unique stage in the life cycle of flowering plants. During seed germination, the embryo undergoes major developmental transitions to become a seedling. Studying gene expression in individual seed cell types has been challenging due to the lack of spatial information or low throughput of existing methods. To overcome these limitations, a spatial transcriptomics workflow was developed for germinating barley grain. This approach enabled high-throughput analysis of spatial gene expression, revealing specific spatial expression patterns of various functional gene categories at a sub-tissue level. This study revealed over 14000 genes differentially regulated during the first 24 hafter imbibition. Individual genes, such as the aquaporin gene family, starch degradation, cell wall modification, transport processes, ribosomal proteins and transcription factors, were found to have specific spatial expression patterns over time. Using spatial autocorrelation algorithms, we identified auxin transport genes that had increasingly focused expression within subdomains of the embryo over time, suggesting their role in establishing the embryo axis. Overall, our study provides an unprecedented spatially resolved cellular map for barley germination and identifies specific functional genomics targets to better understand cellular restricted processes during germination. The data can be viewed at https://spatial.latrobe.edu.au/.

Evaluation of cyclic repetition frequency based algorithm for fetal heart rate extraction from fetal phonocardiography

The passive and non-invasive approach of fetal heart rate (FHR) estimation using fetal phonocardiography (fPCG) provides an alternative to the commonly used Doppler ultrasound-based technique. The acoustic signal recorded on the mother’s abdomen is corrupted by noise, making FHR estimation difficult. Thus, an accurate and reliable FHR measurement algorithm from the fPCG signal is needed. This study evaluates a cyclic repetition frequency-based algorithm with additional FHR validation steps on the clinical recordings from three datasets with varying signal quality. The FHR from the algorithms is compared against the reference FHR measurements, and the accuracy and reliability performance measure is determined. The cyclic repetition algorithm significantly improves (p < 0.05) against the Hilbert-envelope autocorrelation algorithm for poor-quality signals. The algorithm's average reliability across three datasets is 93%, and accuracy is 4.5 beats per minute. The algorithm performs well for the gestation week ranging from 26 −40 and for the labor and antenatal ward data.

Development of a grain-size determination method based on digital images of muddy sediments in coastal areas

The grain-size distribution is a fundamental parameter for assessment of coastal environments. In coastal areas, grain-size analysis of sediments is hampered by arduous collection of sediments from the seafloor at each sampling site and long analysis time. Although rapid determination methods using digital imaging have been developed over the last two decades, these methods cannot be applied to muddy sediments in coastal areas. In the present study, a digital image analysis method applicable to muddy sediments was developed based on sediment color analysis combined with an autocorrelation algorithm introduced by Rubin (2004). For digital image collection in the laboratory, a waterproof digital camera (16 megapixels) was used; it was placed 9 cm from the sediment surface. The imaging equipment consisted of the digital camera, an acrylic plate, and a housing filled with tap water, which was used to avoid effects of suspended sediment in the seawater. Model sediments were prepared and images of the sediment surfaces were taken. The mud and coarse fraction of the sediment were successfully estimated through binarization of the image based on brightness values and the grain-size distribution of the coarse fraction was determined using the autocorrelation algorithm introduced by Rubin (2004). In the field, the imaging apparatus consisting of a stainless-steel cage, lighting equipment, a housing filled with tap water, and a wide acrylic plate attached to the bottom of the housing provided a sturdy structure for obtaining clear digital images from uneven field sediments with suspended particles in the bottom water. The ship was moved several hundred meters at a time; the imaging apparatus was allowed to settle on the sediments and images were then taken continuously at set intervals. From these images, grain-size distributions of sediments containing >15% mud were successfully estimated. Thus, the digital image analysis method developed here will be suitable for measuring the fraction of surface area covered by mud and the size of the visible coarse fraction, and will be useful for monitoring of various sediment types, including muddy coastal sediments, demonstrating great promise for elucidating benthic ecosystem structures and functions in coastal areas.

Field programmable gate array compression for large array multispeckle diffuse correlation spectroscopy.

Diffuse correlation spectroscopy (DCS) is an indispensable tool for quantifying cerebral blood flow noninvasively by measuring the autocorrelation function (ACF) of the diffused light. Recently, a multispeckle DCS approach was proposed to scale up the sensitivity with the number of independent speckle measurements, leveraging the rapid development of single-photon avalanche diode (SPAD) cameras. However, the extremely high data rate from advanced SPAD cameras is beyond the data transfer rate commonly available and requires specialized high-performance computation to calculate large number of autocorrelators (ACs) for real-time measurements. We aim to demonstrate a data compression scheme in the readout field-programmable gate array (FPGA) of a large-pixel-count SPAD camera. On-FPGA, data compression should democratize SPAD cameras and streamline system integration for multispeckle DCS. We present a SPAD array with 128 linear ACs embedded on an FPGA to calculate 12,288 ACFs in real time. We achieved a signal-to-noise ratio (SNR) gain of 110 over a single-pixel DCS system and more than threefold increase in SNR with respect to the state-of-the-art multispeckle DCS. The FPGA-embedded autocorrelation algorithm offers a scalable data compression method to large SPAD array, which can improve the sensitivity and usability of multispeckle DCS instruments.

Multicharacteristics Arc Model and Autocorrelation-Algorithm Based Arc Fault Detector for DC Microgrid

DC microgrids contribute to the increasing penetration of renewable energies. However, a DC arc fault will lead to malfunctions and even fire hazards in a DC microgrid. To ensure the safe and efficient operation of a DC microgrid, the characteristics of DC arc faults should be determined, and a reliable arc-fault detection technique is required. Nevertheless, it is difficult to investigate the DC arc faults in a practical system. Thus, an accurate and reliable arc model for simulation research of arc faults is significant. Since a DC microgrid has numerous branches, the existing arc models cannot express the complex and random characteristics of potential arc faults. Besides, the detection points need to be determined for the appropriate installation of arc fault detectors. Furthermore, the arc features may be ambiguous and affected by the environmental noise and power electronics noise leading to nuisance tripping to arc detectors. In this paper, a multi-characteristics arc model is established based on the volt-ampere, current sag, and power spectral characteristics of arc faults. According to the frequency domain features of arc faults and interaction effects between different branches, the arc-detection-point selection principle is formed. A non-invasive arc fault detector based on magnetic-field sensing and autocorrelation algorithm is developed. It can avoid the effects of periodic environmental noise and power electronics noise by comparing the correlation between arc features in periods. The experimental results verify that the arc faults can be detected with high accuracy by installing the autocorrelation-algorithm-based arc detector at detection points selected by the arc-detection-point selection principle in a DC microgrid.

Multimodal spatial-temporal feature representation and its application in action recognition

目的 在人体行为识别研究中，利用多模态方法将深度数据与骨骼数据相融合，可有效提高动作的识别率。针对深度图像信息数据量大、冗余度高等问题，提出一种通过获取关键时程信息动作帧序列降低冗余的算法，即质心运动路径松弛算法，并根据不同模态数据的特点，提出一种新的时空特征表示方法。方法 质心运动路径松弛算法根据质心在相邻帧之间的运动距离，计算图像差分后获得的活跃部分的相似系数，然后剔除掉相似度高的帧，获得足以表达行为的关键时程信息。根据图像动态部分的变化特性、人体各部分在运动中的协同性和局部显著性特征构建一种新的时空特征表示方法。结果 在MSR-Action3D数据集上对本文方法的效果进行验证。在3个子集中进行交叉验证的平均分类识别率为95.743 2%，分别比Multi-fused，CovP3DJ，D3D-LSTM（densely connected 3DCNN and long short-term memory），Joint Subset Selection方法高2.443 2%，4.763 2%，0.343 2%，0.213 2%。本文方法在使用完整数据集的扩展实验中进行交叉验证的分类识别率为93.040 3%，具有很好的鲁棒性。结论 实验结果表明，本文提出的去冗余算法在降低冗余后提升了识别效果，提取的特征之间具有相关性低的特点，在组合识别中具有良好的互补性，有效提高了分类识别的精确度。;Objective Human body motion-related recognition has been developing in the context of computer vision and pattern recognition like auxiliary human-computer interaction，motion analysis，intelligent monitoring，and virtual reality. To obtain two-dimensional information for its behavioral recognition，conventional motion behavior recognition is mainly used the RGB image sequence captured by RGB camera. To improve the ability to detect short-duration fragments，current feature descriptors for RGB image sequences are employed to characterize human behavior，such as histogram of oriented gradient（HOG），histogram of optical flow （HOF），and a three-dimensional feature pyramid. Some researchers are focused on the feature that image depth is insensitive to ambient light since RGB images are oriented to behavior image sequences of objects in terms of two-dimensional information. The depth information of the image is coordinated with the features of RGB image to describe the related behavior. Human behavior recognition-relevant multi-modal method can be used to fuse depth data and skeleton data，which can improve the recognition rate of action effectively. Recent depth map is widely used in relevant to human behavior recognition. But，the collection of depth information data is required to be optimized because of time complexity of feature extraction and space complexity of feature storage. To resolve the problems，we develop an algorithm to optimize frames of the depth map and resource consumption. At the same time，a new representation of motion features is facilitated as well according to the motion information of the centroid. Method First，the temporal feature vector is used in terms of depth map sequence-extracted time sequence information. The centroid motion path relaxation algorithm is used to realize depth image de-duplication and de redundancy，and the skeleton map-extracted spatial structure feature vector from are spliced to form the spatio-temporal feature input. Next，spatial features are extracted in terms of the original skeleton points coordinates-spliced three-channel spatial feature map. Finally，the fusion probability of spatio-temporal features and spatial features is used for classification and recognition. Our centroid motion path relaxation algorithm is focused on the optimization of redundant information，the time complexity of feature extraction，and the space complexity of feature storage. For the skeleton data，the global feature of motion direction is proposed to fully reflect the integrity and coordination of limb movements. The extracted features are concatenated to obtain the spatio-temporal feature vector，and they can be fused and enhanced through the original coordinates of skeleton points-built three-channel spatial feature map. Its effectiveness is verified on the MSR-Action3D dataset. Result The experimental setting 1 demonstrate that it is 0. 826 0% higher than the depth motion map（DMM）-local binary pattern（LBP）algorithm，1. 015 2% higher than DMM-CRC（collaborative representation classifier），3. 450 1% higher than gradient local auto correlation（DMM-GLAC） algorithm，0. 605 8% higher than EigenJoint algorithm，and 0. 605 8% higher than space-time auto correlation of gradient （STACOG）algorithm is 10. 624 5% higher. After removing redundancy，the result of experimental setting 1 is 0. 126 1% higher as well. The cross-validation on experimental setting 2 show that the average classification and recognition rate in the three subsets is 95. 743 2%，2. 443 2% higher than multi-fused method，4. 763 2% higher than CovP3DJ method，0. 343 2% higher than D3D-LSTM method，and 0. 213 2% higher than joint subset selection method. For the overall data set，it is 2. 030 3% higher than low latency method，0. 240 3% higher than combination of deep models method，and 2. 340 3% higher than complex network coding method. The experimental setting 2 illustrates that the average classification recognition rate of cross-validation in three subsets is 95. 743 2%，and the classification recognition rate of the complete dataset is 93. 040 3%. Conclusion Our algorithm proposed can improve the recognition effect based on redundancy-optimized，and the featuresextracted have lower correlation mutually，which can improve the accuracy of classification recognition effectively.

INTERFERENCE RESISTANCE OF A CORRELATION RECEIVER AT A LIMITED OBSERVATION INTERVAL

The article considers the possibility of increasing the capability of detecting a useful radio signal against the background of noise and simultaneously reducing the possibility of mistaking noise fluctuations for a signal. Moreover, if it is absent then the Neumann-Pearson statistical test should be used. This criterion is a partial case from the class of criteria consisting in the calculation of the likelihood ratio and does not require knowledge of a priori probabilities of the presence or absence a useful signal while constructing an optimal receiver and Bayesian methods are used.&#x0D; The effectiveness of the detection procedure according to the Neumann-Pearson test is characterized by the probability of correct detection at a fixed false alarm probability. These probabilities depend on the signal-to-noise ratio at the output of the detection device. So, an important preparatory stage of the reception immunity analysis is the study of this parameter which is very important in the section of statistical radio engineering.&#x0D; It is known that the optimal detection of a signal against a background of white noise is reduced to the correlation integral calculation. This calculation can be done directly by using a multiplier and integrator (correlation method), by using a filter that is optimal for this signal (filter method) or by using a correlation-filter method.&#x0D; The presence of a priori uncertainty during signal reception makes it impossible to create an optimal (consistent) receiving path and therefore makes it impossible to obtain the required value of the signal-to-noise ratio at the output of the detection device. In this case, an autocorrelation algorithm is used which is resistant to a priori uncertainty of signal parameters and is “optimal” while detecting signals of unknown shape. The most general character for determining the signal/noise ratio has an expression for the case of an incoherent single-channel autocorrelation algorithm for detecting a noisy signal against a background of stationary white noise (interference).&#x0D; For example, quite a large number of scientific works are devoted to the interference immunity issue of the correlation reception method. They mainly consider the interference immunity of correlation receivers when a mixture of harmonic signal and noise passes through them. The circuits with the same average frequencies of multiplying processes and etc. are analyzed. There are also known works in which formulas relating the value of the signal-to-noise ratio at the output of the receiving path for a complex signal to the input signal-to-noise ratio are given.&#x0D; In the above works, idealized cases are considered, namely, when the observation is conducted on a fairly long observation interval but the real observation interval is quite limited. In this case, the averaging is carried out over a limited time interval and it is necessary to take into account the intercorrelation relations between the signal and noise, therefore, along with the signal-signal, noise-noise, signal-noise and noise-signal components, we will have the following mutual correlation moments between the combinations of signal and noise component.&#x0D; The issue of an autocorrelation receiver when passing an additive mixture of signal and white noise at a limited observation interval is investigated in which the immunity index is considered and a general solution of the signal/noise ratio based on the deviation criterion is obtained. Therefore, obtaining an analytical expression that establishes the relationship between the signal-to-noise ratio at the input and output of the receiver is very important and relevant.&#x0D; The purpose of the article is to obtain an analytical expression of the signal-to-noise ratio value at the threshold device input of an incoherent autocorrelation receiver at a limited observation interval.

Research on IEEE 802.11 OFDM Packet Detection Algorithms for Household Wireless Sensor Communication

Millions of smart home devices equipped with wireless sensors have gradually entered people’s homes and improved the quality of life. Wireless communication with sensors is crucial for remote automatic control of smart devices. Packet detection is one of the key technologies in wireless communication systems and faces the challenges of detection accuracy and power consumption. In households, WiFi devices are widely deployed. Therefore, we focus on IEEE 802.11 OFDM packet detection algorithms for household wireless sensor communication. We first introduce four packet detection algorithms and verify their feasibility by simulation experiments. Then, we identify two important factors that affect detection accuracy and provide suggestions for algorithm improvement. Further, we compare and discuss the performance of the four algorithms from three dimensions in detail. The results show that each algorithm has its own advantages, and the auto-correlation algorithm has the best overall performance. Finally, we point out the open challenges and future research directions in this field.

Heart rate detection using single-channel Doppler radar system.

A number of algorithms have been developed to extract heart rate from physiological motion data with the Doppler radar system. Yet, it is very challenging to elimi-nate the noise associated with surroundings, especially with a single-channel Doppler radar system. However, single-channel Doppler radars provide the advantage of operating at lower power. Additionally, the heart rate extraction using single-channel Doppler radar has remained somewhat unexplored. This has motivated us to develop effective signal processing algorithms for signals received from single-channel Doppler radars. In this paper, we have proposed and studied three algorithms for estimating heart rate. The first algorithm is based on applying FFT on an FIR filtered signal. In the second algorithm, autocorrelation was performed on the filtered data. Thirdly, we used a peak finding algorithm in conjunction with a moving average preceded by a clipper to determine the heart rate. The results obtained were compared with the heart rate readings from a pulse oximeter. With a mean difference of 2.6 bpm, the heart rate from Doppler radar matched that from the pulse oximeter most frequently when the peak finding algorithm was used. The results obtained using autocorrelation and peak finding algorithm (with standard deviations of 2.6 bpm and 4.0 bpm) suggest that a single channel Doppler radar system can be a viable alternative to contact heart rate monitors in patients for whom contact measurements are not feasible.