Deep Learning Based Stage-wise Two-dimensional Speaker Localization with Large Ad-hoc Microphone Arrays

While deep-learning-based speaker localization has shown advantages in challenging acoustic environments, it often yields only direction-of-arrival (DOA) cues rather than precise two-dimensional (2D) coordinates. To address this, we propose a novel deep-learning-based 2D speaker localization method leveraging ad-hoc microphone arrays, where an ad-hoc microphone array is composed of randomly distributed microphone nodes, each of which is equipped with a traditional array. Specifically, we first employ convolutional neural networks at each node to estimate speaker directions. Then, we integrate these DOA estimates using triangulation and clustering techniques to get 2D speaker locations. To further boost the estimation accuracy, we introduce a node selection algorithm that strategically filters the most reliable nodes. Extensive experiments on both simulated and real-world data demonstrate that our approach significantly outperforms conventional methods. The proposed node selection further refines performance. The real-world dataset in the experiment, named Libri-adhoc-node10 which is a newly recorded data described for the first time in this paper, is online available at https://github.com/Liu-sp/Libri-adhoc-nodes10

Application of Near-infrared Spectroscopy and Multiple Spectral Algorithms to Explore the Effect of Soil Particle Sizes on Soil Nitrogen Detection

Soil nitrogen is the key parameter supporting plant growth and development; it is also the material basis of plant growth. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near-infrared (NIR) spectroscopy is widely used for rapid detection of soil nutrients. In this study, the variation law of soil NIR reflectivity spectra with soil particle sizes was studied. Moreover, in order to precisely study the effect of particle size on soil nitrogen detection by NIR, four different spectra preprocessing methods and five different chemometric modeling methods were used to analyze the soil NIR spectra. The results showed that the smaller the soil particle sizes, the stronger the soil NIR reflectivity spectra. Besides, when the soil particle sizes ranged 0.18–0.28 mm, the soil nitrogen prediction accuracy was the best based on the partial least squares (PLS) model with the highest Rp2 of 0.983, the residual predictive deviation (RPD) of 6.706. The detection accuracy was not ideal when the soil particle sizes were too big (1–2 mm) or too small (0–0.18 mm). In addition, the relationship between the mixing spectra of six different soil particle sizes and the soil nitrogen detection accuracy was studied. It was indicated that the larger the gap between soil particle sizes, the worse the accuracy of soil nitrogen detection. In conclusion, soil nitrogen detection precision was affected by soil particle sizes to a large extent. It is of great significance to optimize the pre-treatments of soil samples to realize rapid and accurate detection by NIR spectroscopy

Analysis of Sildenafil in Liquor and Health Wine Using Surface Enhanced Raman Spectroscopy

The illegal adulteration of sildenafil in herbal food supplements and alcoholic drinks immensely threatens human health due to its harmful side-effects. Therefore, it is important to accurately detect and identify the presence of sildenafil in alcoholic drinks. In this study, Opto Trace Raman 202 (OTR 202) was used as surface enhanced Raman spectroscopy (SERS) active colloids to detect sildenafil. The results demonstrated that the Raman enhancement factor (EF) of OTR 202 colloids reached 1.84 × 107 and the limits of detection (LODs) of sildenafil in health wine and liquor were found to be as low as 0.1 mg/L. Moreover, the SERS peaks of 645, 814, 1235, 1401, 1530 and 1584 cm−1 could be qualitatively determined as sildenafil characteristic peaks and the relationship between Raman peak intensity and sildenafil concentration in health wine and liquor were different. There was a good linear correlation between Raman peak intensity, and sildenafil concentration in health wine ranged 0.1–1 mg/L (0.9687< R2 < 0.9891) and 1–10 mg/L (0.9701 < R2 < 0.9840), and in liquor ranged 0.1–1 mg/L (0.9662 < R2 < 0.9944) and 1–20 mg/L (0.9625 < R2 < 0.9922). The relative standard deviations (RSD) were less than 5.90% (sildenafil in health wine) and 9.16% (sildenafil in liquor). The recovery ranged 88.92–104.42% (sildenafil in health wine) and 90.09–104.55% (sildenafil in liquor). In general, the sildenafil in health wine and liquor could be rapidly and quantitatively determined using SERS technique, which offered a simple and accurate alternative for the determination of sildenafil in alcoholic drinks

FATIGUE EVALUATION OF ELECTRIC SHOVEL HANDLE WELDED STRUCTURE BASED ON BATTLLE STRUCTURAL STRESS APPROACH

To calculate the fatigue strength of complex handle welded structure under cyclic loading,the stress time history of handle component under cyclic loading was solved using transient modal dynamic analysis method,and the fatigue life of welded structure was calculated using mesh-size insensitive Battlle structural stress approach.It was found that the fatigue strength of original handle welded structure is insufficient because the ear plate connection structure is inconsistent,which results in stress concentration.The transition plate was added to the improved welded structure and the calculation result shows the maximum structural stress near the weld lines is 44.9% lower than that of the original structure,and the stress is relatively uniform.The fatigue life of the improved one was estimated to be 4 years and 5 months.The weld failure of improved welded structure has not occurred for more than 3 years in service in two equipments,which meets the application requirements.For large welded structures which are difficult to conduct physical tests,the numerical calculation based on Battlle structural stress approach can provide a basis for structural design

Spectral Analysis and Sensitive Waveband Determination Based on Nitrogen Detection of Different Soil Types Using Near Infrared Sensors

Compared with the chemical analytical technique, the soil nitrogen acquisition method based on near infrared (NIR) sensors shows significant advantages, being rapid, nondestructive, and convenient. Providing an accurate grasp of different soil types, sensitive wavebands could enhance the nitrogen estimation efficiency to a large extent. In this paper, loess, calcium soil, black soil, and red soil were used as experimental samples. The prediction models between soil nitrogen and NIR spectral reflectance were established based on three chemometric methods, that is, partial least squares (PLS), backward interval partial least squares (BIPLS), and back propagation neural network (BPNN). In addition, the sensitive wavebands of four kinds of soils were selected by competitive adaptive reweighted sampling (CARS) and BIPLS. The predictive ability was assessed by the coefficient of determination R2 and the root mean square error (RMSE). As a result, loess ( 0.93 < R p 2 < 0.95 , 0.066 g / kg < RMSE p < 0.075 g / kg ) and calcium soil ( 0.95 < R p 2 < 0.96 , 0.080 g / kg < RMSE p < 0.102 g / kg ) achieved a high prediction accuracy regardless of which algorithm was used, while black soil ( 0.79 < R p 2 < 0.86 , 0.232 g / kg < RMSE p < 0.325 g / kg ) obtained a relatively lower prediction accuracy caused by the interference of high humus content and strong absorption. The prediction accuracy of red soil ( 0.86 < R p 2 < 0.87 , 0.231 g / kg < RMSE p < 0.236 g / kg ) was similar to black soil, partly due to the high content of iron–aluminum oxide. Compared with PLS and BPNN, BIPLS performed well in removing noise and enhancing the prediction effect. In addition, the determined sensitive wavebands were 1152 nm–1162 nm and 1296 nm–1309 nm (loess), 1036 nm–1055 nm and 1129 nm–1156 nm (calcium soil), 1055 nm, 1281 nm, 1414 nm–1428 nm and 1472 nm–1493 nm (black soil), 1250 nm, 1480 nm and 1680 nm (red soil). It is of great value to investigate the differences among the NIR spectral characteristics of different soil types and determine sensitive wavebands for the more efficient and portable NIR sensors in practical application

Gold Nanoparticles with Different Particle Sizes for the Quantitative Determination of Chlorpyrifos Residues in Soil by SERS

Chlorpyrifos (CPF) is widely used in the prevention and control of crop pests and diseases in agriculture. However, the irrational utilization of pesticides not only causes environmental pollution but also threatens human health. Compared with the conventional techniques for the determination of pesticides in soil, surface-enhanced Raman spectroscopy (SERS) has shown great potential in ultrasensitive and chemical analysis. Therefore, this paper reported a simple method for synthesizing gold nanoparticles (AuNPs) with different sizes used as a SERS substrate for the determination of CPF residues in soil for the first time. The results showed that there was a good linear correlation between the SERS characteristic peak intensity of CPF and particle size of the AuNPs with an R2 of 0.9973. Moreover, the prepared AuNPs performed great ultrasensitivity, reproducibility and chemical stability, and the limit of detection (LOD) of the CPF was found to be as low as 10 μg/L. Furthermore, the concentrations ranging from 0.01 to 10 mg/L were easily observed by SERS with the prepared AuNPs and the SERS intensity showed a good linear relationship with an R2 of 0.985. The determination coefficient (Rp2) reached 0.977 for CPF prediction using the partial least squares regression (PLSR) model and the LOD of CPF residues in soil was found to be as low as 0.025 mg/kg. The relative standard deviation (RSD) was less than 3.69% and the recovery ranged from 97.5 to 103.3%. In summary, this simple method for AuNPs fabrication with ultrasensitivity and reproducibility confirms that the SERS is highly promising for the determination of soil pesticide residues

Influence of Multiple Factors on the Wettability and Surface Free Energy of Leaf Surface

The wettability of plant leaves directly reflects leaf hydrophilicity, which is the key factor that influences the adhesion of liquid pesticide as well as affects plant protection products (PPP) efficacy. Generally, the wettability of leaf surface is quantified by the contact angle and surface free energy (SFE), which are mainly dependent on leaf surface properties, liquid properties and other spraying parameters. Therefore, the aim of this paper was to investigate the SFE of rice and rape leaves with the variation of leaf status, leaf surface, and probe liquid as well as the influence of droplet falling height, solid surface, and PPP concentration on the wettability. The results showed that: (1) the dispersive components of SFE of rice and rape account for a large proportion which are closely related to their hydrophobicity—the abaxial of rape new leaf and the adaxial of rape old leaf are easier to wet comparing with rice and rape leaves in other statuses; (2) the increase of droplet falling height had a significant effect on improving the wettability between wax surface and adjuvant solution, while it had little improving effect on the wettability between wax surface and water; (3) the wettability of different solid surface varied greatly, and the order of wettability from good to bad is water-sensitive paper (WSP), wax, rape leaf, and rice leaf; (4) the effect of PPP concentration on the leaf surface wettability is significant, the contact angle decreased with the increase of PPP concentration, and the wettability of microemulsion is better than that of suspending agent and wettable powder. In conclusion, the SFE and wettability of crop leaf surface determine the suitable type of PPP, studying the influence of multiple factors on leaf surface wettability can provide a reliable reference for providing scientific guidance as well as improving the effective utilization of PPP

Research on the Effects of Drying Temperature on Nitrogen Detection of Different Soil Types by Near Infrared Sensors

Soil is a complicated system whose components and mechanisms are complex and difficult to be fully excavated and comprehended. Nitrogen is the key parameter supporting plant growth and development, and is the material basis of plant growth as well. An accurate grasp of soil nitrogen information is the premise of scientific fertilization in precision agriculture, where near infrared sensors are widely used for rapid detection of nutrients in soil. However, soil texture, soil moisture content and drying temperature all affect soil nitrogen detection using near infrared sensors. In order to investigate the effects of drying temperature on the nitrogen detection in black soil, loess and calcium soil, three kinds of soils were detected by near infrared sensors after 25 °C placement (ambient temperature), 50 °C drying (medium temperature), 80 °C drying (medium-high temperature) and 95 °C drying (high temperature). The successive projections algorithm based on multiple linear regression (SPA-MLR), partial least squares (PLS) and competitive adaptive reweighted squares (CARS) were used to model and analyze the spectral information of different soil types. The predictive abilities were assessed using the prediction correlation coefficients (RP), the root mean squared error of prediction (RMSEP), and the residual predictive deviation (RPD). The results showed that the loess (RP = 0.9721, RMSEP = 0.067 g/kg, RPD = 4.34) and calcium soil (RP = 0.9588, RMSEP = 0.094 g/kg, RPD = 3.89) obtained the best prediction accuracy after 95 °C drying. The detection results of black soil (RP = 0.9486, RMSEP = 0.22 g/kg, RPD = 2.82) after 80 °C drying were the optimum. In conclusion, drying temperature does have an obvious influence on the detection of soil nitrogen by near infrared sensors, and the suitable drying temperature for different soil types was of great significance in enhancing the detection accuracy

The Effects of Drying Temperature on Nitrogen Concentration Detection in Calcium Soil Studied by NIR Spectroscopy

Soil nitrogen is one of the crucial components for plant growth. An accurate diagnosis based on soil nitrogen information is the premise of scientific fertilization in precision agriculture. Soil nitrogen content acquisition based on near-infrared (NIR) spectroscopy shows the significant advantages of high accuracy, real-time analysis, and convenience. However, soil texture, soil moisture content, and drying temperature all affect soil nitrogen detection by NIR spectroscopy. In order to investigate the effects of drying temperature on calcium soil nitrogen detection and its characteristic bands, soil samples were detected at a 25 °C placement (ambient temperature) after 40 °C drying (medium temperature), 60 °C drying (medium-high temperature), 80 °C drying (high temperature), and 105 °C drying (extreme high temperature), respectively. Besides that, the original spectra were pretreated with five preprocessing methods, and the characteristic variables were selected by competitive adaptive reweighted squares (CARS) and backward interval partial least squares (BIPLS). The partial least squares (PLS) method was used for modeling and analysis. The predictive abilities were assessed using the coefficients of determination (R2), the root mean squared error (RMSE), and the residual predictive deviation (RPD). As a result, the characteristic bands focus on 928–960 nm and 1638–1680 nm when soil was detected after 40 °C, 60 °C, and 80 °C drying. Calcium soil obtained the best prediction accuracy ( R p 2 = 0.966 , R M S E p = 0.128 g kg , R P D = 5.03 ) after 40 °C drying by the method of CARS-BIPLS-PLS. Meanwhile, the prediction model also performed well when soil was detected after 60 °C drying ( R p 2 = 0.946 , R M S E p = 0.172 g / kg , R P D = 4.53 ) and 80 °C drying ( R p 2 = 0.961 , R M S E p = 0.143 g kg , R P D = 4.98 ) . However, the calcium soil obtained the worst detection result when soil was placed at 25 °C. In conclusion, a low or extremely high drying temperature had an adverse influence on the soil nitrogen detection, and the 40 °C drying temperature as well as the CARS-BIPLS-PLS method were optimal to enhance the soil nitrogen detection accuracy