Double Threshold Research Articles

Performance Comparison of Energy Detection Based non – Cooperative Spectrum Sensing Techniques in Cognitive Radio

Cognitive radio is a ground-breaking software-defined radio paradigm that offers Dynamic spectrum access, allowing secondary users to use the frequency band allotted to the principal user when it is not in use and vacate when the prime application returns. The ability to sense the spectrum is critical to cognitive radio's efficiency. Energy detection sensing is the simplest and most often used spectrum sensing approach, owing to its ease of implementation in cognitive radio applications. The three-energy detection-based algorithms adopted for different scenarios have been compared in this study. The algorithms include the double-threshold energy detection, adaptive single threshold energy detection, and the adaptive double threshold spectrum sensing algorithm. Since the noise prediction in the practical situation is difficult, the necessity is to find the best algorithm in this condition. The other equally important parameters for efficiently sensing the spectrum are spectrum efficiency and less interference to the primary user. Simulation findings show that the adaptive double threshold approach outperforms the other two algorithms in all respect. The detection probability of the method is typically found to be substantially greater as compared to other two techniques. In addition, the likelihood of a false alarm is significantly reduced. Furthermore, when the signal-to-noise ratio value is low, often below -5dB, the performance of this approach is poor. MATLAB is used to run all of the simulations.

Can the digital economy development curb carbon emissions? Evidence from China.

“Carbon neutrality, carbon peaking” is China’s national commitment to the whole world about its plans to manage global climate change. China faces many severe challenges in fulfilling its commitments to reduce emissions. China’s digital economy is currently booming, and whether it can provide opportunities for reducing regional carbon emissions is worth exploring. This study constructed a comprehensive system to evaluate the development of its digital economy based on China’s regional data and empirically tested the direct, indirect, and spatial effects of the comprehensive development of digital economy on regional carbon emissions. In addition, it examined the special stage characteristics using a Hansen threshold model. This study found the following: first, the digital economy significantly suppresses carbon emissions in general, notably with a spatial spillover effect to neighboring provinces. Secondly, an analysis of the mechanism shows that the comprehensive development of a digital economy can restrain regional carbon emissions through industrial progress and the optimization of energy consumption. Third, there are double thresholds, special driving trends and an “inverted N-type” relationship with development. Fourth, a spatial heterogeneity analysis revealed that significant “local” and “neighboring” impacts on the reduction of carbon emissions only exist in the central and eastern areas. This study has a reference value for releasing the dividend of digital economy development and reducing carbon emissions.

Impacts of green finance on green innovation: A spatial and nonlinear perspective

This paper provides an in-depth understanding of the mechanisms of green finance on green innovation, focusing on their spatial and non-linear relationships. Using panel data of 30 provinces of China during the period of 2009–2017, a green finance index is constructed, and the spatial Durbin model and panel threshold model are used. The results reveal that: (1) Green finance and green innovation had significant positive autocorrelation , and the development of green finance in provinces of central and western China were still in the lower-level range. (2) The coefficients of the direct effect and indirect effect of green finance on green innovation were significantly positive, which indicated that green finance was conducive to enhancing green innovation in local and adjacent provinces during the research period. (3) Double threshold effects of green finance on green innovation were observed, which demonstrated that the driving effect of green finance was decreased with the raising of intensity of environmental regulation. The implication of these results is that government should make full use of the radiation and trickle-down functions of green finance and green innovation to narrow regional gaps in sustainable development while maintaining a moderate intensity of environmental regulation. • A green finance index of 30 provinces of China during the period of 2009 to 2017 was constructed on the basis of green credit, green securities, green insurance, and green investment. • The heterogeneity of region and time for green finance were analyzed employing the spatial exploratory analysis, which manifested that the spatial evolution of green finance in China presents a gradient decreasing pattern from east to west. • The impact of green finance on green innovation was explored by the spatial Durbin model, which proved that the direct effect and the spatial spillover effect were significantly positive. • Moderate intensity of environmental regulation helps green finance promote green innovation and this value should not exceed a maximum of 0.935.

Improved Registration Algorithm Based on Double Threshold Feature Extraction and Distance Disparity Matrix.

Entire surface point clouds in complex objects cannot be captured in a single direction by using noncontact measurement methods, such as machine vision; therefore, different direction point clouds should be obtained and registered. However, high efficiency and precision are crucial for registration methods when dealing with huge number of point clouds. To solve this problem, an improved registration algorithm based on double threshold feature extraction and distance disparity matrix (DDM) is proposed in this study. Firstly, feature points are extracted with double thresholds using normal vectors and curvature to reduce the number of points. Secondly, a fast point feature histogram is established to describe the feature points and obtain the initial corresponding point pairs. Thirdly, obviously wrong corresponding point pairs are eliminated as much as possible by analysing the Euclidean invariant features of rigid body transformation combined with the DDM algorithm. Finally, the sample consensus initial alignment and the iterative closest point algorithms are used to complete the registration. Experimental results show that the proposed algorithm can quickly process large data point clouds and achieve efficient and precise matching of target objects. It can be used to improve the efficiency and precision of registration in distributed or mobile 3D measurement systems.

Co-Occurrence-Based Double Thresholding Method for Research Topic Identification

Identifying possible research gaps is a main step in problem framing, however it is increasingly tedious and expensive considering the continuously growing amount of published material. This situation suggests the critical need for methodologies and tools that can assist researchers in their selection of future research topics. Related work mostly focuses on trend analysis and impact prediction but less on research gap identification. This paper presents our first approach in automated identification of feasible research gaps by using a double-threshold procedure to eliminate the research gaps that are currently difficult to study or offer little novelty. Gaps are then found by extracting subgraphs for the less-frequent co-occurrences and correlations of key terms describing domains. A case study applying the methodology for electronic design automation (EDA) domain is also discussed in the paper.

Natural resource dependence and economic growth in SSA: are there threshold effects?

ABSTRACT The debate on the natural resource curse hypothesis has attracted the attention of policy makers and policy analysts for the past few decades. However, the empirical findings on such a hypothesis have proven inconclusive. Our study investigates the threshold effects of natural resource dependence on economic growth in sub-Sahara Africa (SSA) using both aggregate and disaggregate data from 1990 to 2019 by employing a threshold effect model. The results indicate a double threshold effect of natural resource rent on economic growth. In particular, below 6% of GDP, aggregate natural resource rent exerts a significant negative effect on economic growth. However, as the rents increase above 6% to about 15% of GDP its negative effect on economic growth significantly reduces. In addition, beyond 15% of GDP natural resource rent exhibit a substantial significant positive impact on economic growth. Further, the disaggregated data reveal that forest rents exhibit a strong weighty adverse effect on economic growth at all levels of thresholds. The study recommends that governments within the sub-region need to put in policies to ensure that natural resources generate at least 15% of GDP annually to promote growth.

Impact of environmental regulation intensity on green innovation efficiency in the Yellow River Basin, China

In this paper, a model for the slacks-based measure of super-efficiency in data envelopment analysis (SBM-DEA) was used to measure the green innovation efficiency (GIE) of 79 cities in the Yellow River Basin (YRB) from 2003 to 2018. In addition, the panel threshold model was employed to test the nonlinear relationship between environmental regulation intensity (ERI) and GIE. According to the results, the average GIE in the YRB was 0.68. However, the GIE exhibited a rising and fluctuating temporal trend and the following spatial trend: “lower reaches > upper reaches > middle reaches.” Moreover, ERI had a double threshold effect on GIE in the YRB. That is, ERI had a significant positive impact on GIE only when ERI ranged between 0.595 and 0.899. Furthermore, the upper, middle, and lower reaches of the YRB exhibited different nonlinear correlations between ERI and GIE. Therefore, it is essential to implement environmental regulations that are appropriate for the actual conditions of each region. In addition, the per capita income, teacher resources, industrial structure, and actual utilization of foreign capital are significant factors affecting GIE in the YRB. These findings can help governments formulate suitable environmental policies and promote green innovation in the YRB, thereby contributing toward the goal of global sustainable development.

A dynamic view of environmental regulation influence mechanism on manufacturing agglomeration-a case study of the Yangtze River Delta city cluster.

Rapid development and agglomeration of manufacturing industries in China inevitably lead to serious environmental pollution problems. The government restrains development of manufacturing industries through environmental regulation policies, but this may affect manufacturing industries agglomeration. Therefore, studying the impact of environmental regulation (ER) on manufacturing agglomeration (MA) is a critical prerequisite for the sustainability of China's manufacturing industry. Based on the panel data of 41 cities in Yangtze River Delta (YRD) region of China from 2003 to 2017, this paper explores the threshold effect of ER on MA using a panel threshold model and analyzes the heterogeneity impact of ER on MA in different regions and analyzes the dynamic mechanism and contribution of each influencing factor on MA using a panel vector autoregressive model. The results show that (1) there is a double threshold effect of ER on MA, showing an inverted "U"-shaped effect of rising and then falling; there are threshold effects of ER on MA based on foreign direct investment (FDI) and economic development level (EDL). The effect of ER on MA has the threshold effect under different FDI and EDL level, in which the effect based on FDI shows an inverted "U" shape relationship of rising and then falling; the effect based on EDL shows a growing trend. (2) There are 29 cities in YRD region in the "weak regulation" or "strong regulation" stage, both of which are not conducive to MA. (3) ER has a facilitating effect on MA, but with the extension of forecast period, this facilitating effect turns into an inhibiting effect. MA is mainly influenced by itself and transportation conditions (TC). Our study can provide some insights for improving the ecological environment and promoting the development of manufacturing clusters in YRD region.

Long-Short Term Memory Network-Based Monitoring Data Anomaly Detection of a Long-Span Suspension Bridge.

Structural health monitoring (SHM) systems have been widely applied in long-span bridges and a large amount of SHM data is continually collected. The harsh environment of sensors installed at structures causes multiple types of anomalies such as outlier, minor, missing, trend, drift, and break in the SHM data, which seriously hinders the further analysis of SHM data. In order to achieve anomaly detection from a large amount of SHM data, this paper proposes a long-short term memory (LSTM) network-based anomaly detection method. Firstly, the proposed method reduces the workload for preparing training sets. Secondly, the purpose of real-time anomaly detection can be met. Thirdly, the problem of high alarm rate can be avoided by utilizing double thresholds. To validate the effectiveness of the proposed method, a case study of finite element model simulation is firstly introduced, which illustrates the detailed implementation process. Finally, acceleration data from the SHM system of a long-span suspension bridge located in Jiangyin, China is employed. The results show that the proposed method can detect anomaly with high accuracy and identify abnormal accidents such as a ship collision quickly.

Low-carbon spatial differences of renewable energy technologies: Empirical evidence from the Yangtze River Economic Belt

Under the “double carbon” target, renewable energy technologies offer a new low-carbon development path for human society. This paper selects the Yangtze River Economic Belt as the research area, introduces the classification of the renewable energy technologies from the perspective of patents and uses the Super-SBM model, entropy TOPSIS method and spatial Durbin model to empirically analyze the spatial spillover effects of the renewable energy technologies on carbon emission efficiency (CEE) and optimal development range. The analysis conclusions are as follows: First, there are differences in geographical location and geographic distance between the eastern and the western regions. The CEE is spatially correlated, but the permeability is weak, which specifically reflects the stepwise difference in the eastern, central and western regions of the spatial dimension and the path correlation in the time dimension. Then, the effects of the renewable energy technologies on CEE are all positive, showing continuous and effective positive promotion effects, but the performance of technology diffusion is poor, the spillover effect on the adjacent regions is not obvious. And finally, the positive promotion effect of the renewable energy technologies on CEE is affected by the interval of its double threshold effect.

Diagnosis of abnormal body temperature based on deep neural network

INTRODUCTION: A method for diagnosing abnormal body temperature based on deep neural network is proposed. OBJECTIVES: To improve the diagnostic accuracy, reduce the false alarm rate, and improve the diagnostic level of abnormal body temperature. METHODS: According to the weight of the temperature sensor node itself and its neighbor nodes, the network trust relationship is established, and the node trust value is output through the combination of decision-making. Use trust value and double threshold to identify and remove malicious nodes, and optimize the network structure. The optimized temperature sensor network is used to collect human body temperature data. RESULTS: A deep neural network is used to construct a diagnosis model of abnormal body temperature, so as to realize the diagnosis of abnormal body temperature. CONCLUSION: The experimental results show that the method in this paper has high diagnostic accuracy, low false positive rate and high diagnostic efficiency, and can improve the diagnostic level of abnormal body temperature.

The impact of capital enrichment on total factor productivity from the perspective of innovation capability

ABSTRACT Innovation capability of manufacturing industry is one of the most important factors that determine a country’s competitiveness. This paper expounds the theoretical and empirical relationship among innovation capability (IC), capital enrichment (CE) and total factor productivity (TFP) by using the yearly panel data of 28 Chinese manufacturing segments from 2011 to 2018. The intermediary effect model and threshold model are used for empirical analysis, findings suggest that 1) IC not only directly promotes TFP but also indirectly through CE, indicating that CE has an intermediary effect on the relationship between IC and TFP. 2) The positive effect of CE on TFP is influenced by the “double threshold effect” of IC. Compared with industries having low IC, this positive effect in high IC industries is first enhanced then weakened and later re-enhanced. 3) There is a significant difference in IC and CE among different manufacturing segments.

Can the Agglomeration of New Energy Industries Improve Environmental Efficiency?—Evidence from China

Improving environmental efficiency is the key to solving the contradiction between economic development and environmental degradation. In the process of realizing the “dual carbon” goal, the development of the new energy industry is typically characterized by having low carbon and is an important way to improve environmental efficiency. In this study, based on panel data from 2009 to 2018, the agglomeration levels of the new energy industry and the levels of environmental efficiency were measured. Moreover, the linear and nonlinear relationships between the agglomeration of the new energy industry and environmental efficiency were investigated, and the mediation effect model was used to verify the mechanism of action from a two-dimensional perspective. The results showed that (1) the levels of the agglomeration of the new energy industry (NEA) and environmental efficiency (EE) have generally increased with obvious “regional heterogeneity” and “polar characteristics”; (2) NEA can promote EE, with a marginal effect of 0.0347, but a strong positive driving effect only exists in the eastern region at present, which is higher than the national average level; (3) there is a double threshold effect within the process and the driving trend is “weak negative → weak positive → strong positive"; and (4) the advancement of industrial structures and direct foreign investments are the critical intermedia. Accordingly, this paper puts forward corresponding policy recommendations.

Gender determination of domestic chicks based on vocalization signals

Gender determination in chicks is an important task in poultry production and is helpful for precision feeding of different sexes. At present, most chicken sex identification methods need to be completed manually by professionals, which is time-consuming and laborious. In this paper, a method was designed to determine the sex of one-day-old chicks according to vocalizations. This method uses sound technology to detect chick vocalization and automatically detects vocalization endpoints by the double threshold method using three parameters: short-term energy, short-term zero crossing rate and duration. The audio features were extracted as the input of the three deep learning models for learning and classification. In the experiment, the training set was used to train the model, and the test set was used to calculate the detection results. The vocalizations of the training set and test set came from different chicks. In the gender detection of each vocalization, the accuracy of convolutional neural networks (CNN), long short-term memory (LSTM), and gate recurrent unit (GRU) was 74.55%, 75.73% and 76.15%, respectively. The highest recall was 77.03% of GRU, and the highest specificity was 78.38% of LSTM. After that, the gender of chicks was predicted according to the vocalization detection results. The average accuracy values of CNN, LSTM and GRU for vocalization were 91.25%, 87.08% and 88.33%, respectively. The experimental results show that the method proposed in this paper can be used to detect the gender of chicks by vocalization, which is of great significance for automatic chick gender detection and intelligent poultry production.

A Novel Double Threshold-Based Spectrum Sensing Technique at Low SNR Under Noise Uncertainty for Cognitive Radio Systems.

Cognitive Radio is a novel concept that has invoked a paradigm shift in wireless communication and promises to solve the problem of spectrum underutilization. Spectrum sensing plays a pivotal role in a cognitive radio system by detecting the vacant spectrum for establishing a communication link. For any spectrum sensing method, detection probability and error probability portray a significant part in quantifying the detection performance. At low SNR, it becomes cumbersome to differentiate noise and signal due to which sensing method loses robustness and reliability. In this paper, mathematical modeling and critical measurement of detection probabilities has been done for energy detection-based spectrum sensing at low SNR in uncertain noisy environment. A mathematical model has been proposed to compute double thresholds for reliable sensing when the observed energy is less than the uncertainty in the noise power. A novel parameter “Threshold Wall” has been formulated for optimum threshold selection to overcome sensing failure. Comparative simulation and analytical result measurements have been presented that reveals improved sensing performance.Please check inserted city is correct for affiliation 1.Noida, it is correct

The nonlinear impact of FinTech on the real economic growth: evidence from China

ABSTRACT Financial technology (FinTech) in promoting the real economy is a topic attracting much attention. This paper adopts a threshold regression model to solve the endogenous problem well and make up for the lack of empirical evidence of scientific models in existing research in this area. The empirical results show that FinTech has a significant promoting effect on real economic growth, manifested as a U-shaped relationship and double threshold effect. In the early stage of FinTech development, it will restrain economic growth. The continuous improvement will positively impact economic growth, and the result shows a law of marginal decline. Moreover, there are significant regional differences in the nonlinear characteristics. Our research has solid practical significance and contributes to the literature on the effects of FinTech on real economic growth.

Non-destructive detection of egg qualities based on hyperspectral imaging

Egg quality detection is important to food processing and people consumption. The aim of this study is to detect egg freshness, scattered yolk and eggshell cracks by applying hyperspectral imaging (HSI), multivariate analysis and image process. The transmission visible-near infrared hyperspectral images of egg samples were acquired in the wavelength range of 401–1002 nm. Standard normal variate (SNV) was applied to normalize the spectral data, and iteratively retains informative variable (IRIV) was used to optimize wavelength selection. Based on the feature wavelengths, egg freshness quantitative model was established by using Extreme Gradient Boosting (XGBoost), with coefficient of determination for prediction ( R 2 p ) of 0.91 and root mean square error for prediction (RMSEP) of 4.64. An algorithm including image contrast enhancement, denoising and threshold segmentation was proposed to extract the morphological features of yolk. Based on the morphological feature ratio, the recognition accuracy of scattered yolk eggs reached 97.33%. In addition, a method including crack enhancement, double threshold segmentation was developed to extract the geometric features of cracks. The cracked eggs could be discriminated by XGBoost classification model with identification accuracy of 93.33%. The results indicate that HSI can be useful for the non-destructive detection of egg qualities. • Hyperspectral imaging was applied to detect egg freshness, scattered yolk and cracks. • The feature wavelengths for egg freshness detection were selected by IRIV. • An algorithm for extracting morphological features of yolk was proposed. • A method for extracting geometric features of eggshell cracks was proposed.

Contrast Normalization Filtering Modules for Segmentations of Retinal Blood Vessels from Color Retinal Fundus Images

Vision loss is one of the main complications of eye disease, especially diabetic retinopathy (DR), because DR is a silent disease affecting the retina of the eye and resulting in loss of vision. Manual observation of eye disease takes time and delays effective treatment, so computerized methods are used to diagnose eye disease by extracting their features such as blood vessels, optic disc, and other abnormalities. Many computerized methods are proposed but it is still lacking to obtain small vessels. To overcome this problem, we have proposed methods based on image processing techniques for detection of retinal vessels. The proposed method is based on the elimination of uneven illumination using morphological tactics and principal component analysis. These initial steps are known as the preprocessing module, and our post-processing module contains the vessel coherence and the double threshold binarization method to obtain an image of the segmented vessels. Our proposed method obtained comparable results (average results (sensitivity: 0.78, specificity: 0.95 and precision: 0.951)) compared to the existing methods on the DRIVE and STARE databases.

Sensitive Channel Selection for Mental Workload Classification

Mental workload (MW) assessment has been widely studied in various human–machine interaction tasks. The existing researches on MW classification mostly use non-invasive electroencephalography (EEG) caps to collect EEG signals and identify MW levels. However, the activation region of the brain stimulated by MW tasks is not the same for every subject. It may be inappropriate to use EEG signals from all electrode channels to identify MW. In this paper, an EEG rhythm energy heatmap is first established to visually show the change trends in the energy of four EEG rhythms with time, EEG channels and MW levels. It can be concluded from the presented heatmaps that this change trend varies with subjects, rhythms and channels. Based on the analysis, a double threshold method is proposed to select sensitive channels for MW assessment. The EEG signals of personalized selected channels, named positive sensitive channels (PSCs) and negative sensitive channels (NSCs), are used for MW classification using the Support Vector Machine (SVM) algorithm. The results show that the selection of personalized sensitive channels generally contributes to improving the performance of MW classification.

Double Threshold Energy Based Spectrum Sensing with Copulas

Double Threshold Energy Based Spectrum Sensing with Copulas