Double Threshold Research Articles

Toward a Distribution Difference-Based Passive Motion Detection System Using WiFi Signals

Passive motion detection (PMD) systems based on WiFi signals consider the traditional wireless network as a sensing network for detecting human motion. However, most WiFi-based PMD systems may perform poorly when the deployment environment changes (e.g., changes of transceiver or furniture placement). In addition, under the constraints of the limited devices and measurement noise, developing a robust and accurate motion detector is still heavily required. To this end, we propose R-MoDe to revisit the PMD problem from the perspective of the distribution difference of environmental statistics. First, R-MoDe exploits a statistical model based on temporal correlation function (TCF) to characterize the time-varying channel induced by human motion. Based on the statistical model, R-MoDe calculates environmental statistics by TCF and treats the distribution of the statistics under non-motion scenarios as a template profile, independent of the deployment environment and measurement noise. Next, with the limited devices, we regard available subcarriers as virtual sensors and design a clustering-vote-based sensor selection scheme to boost detection performance. Then, R-MoDe adopts the distance between the distribution of environmental statistics and the template profile as a robust detection metric. Finally, we propose a motion detection strategy based on double thresholds and hysteresis tracking to enable accurate and robust detection. R-MoDe is implemented on commodity WiFi devices in practical indoor environments. Experimental results show that R-MoDe can achieve an average motion detection rate of 96.19% and an average true negative rate of 97.04% using only one pair of transceivers, significantly outperforming the state-of-the-art methods.

Improved Detection Performance of Energy Detection Based Spectrum Sensing in Cognitive Radio Networks

Background: In cognitive radio networks, spectrum sensing plays an important role in identifying the underutilized spectrum bands. Conventional spectrum sensing using energy detection method uses single detection threshold, which degrades the detection performance. Method: Therefore double detection threshold has been proposed for spectrum sensing in the literature to improve the detection performance, but the performance depends on the region between two thresholds termed as confusion state. Hence to improve the overall detection performance new re-sensing scheme has been proposed in this paper by varying the difference between thresholds by an improvement factor K. Results: The proposed method improves the detection performance compared to the single threshold method and double threshold method. Conclusion: Simulation results show that the proposed method operates better than the single threshold energy detection method and improves the detection performance at low signal to noise ratios.

Computing In-Memory Design Based on Double Word Line and Double Threshold 4T SRAM

Computing In-Memory Design Based on Double Word Line and Double Threshold 4T SRAM

Threshold capital structure and predictors of choice between distribution strategies

Orientation: The determination of a threshold capital structure and company specific attributes as predictors of choice between distribution strategies is crucial in the creation of shareholders’ wealth.Research aim: To investigate whether the change in regimes given a threshold capital structure maximises distribution strategies over the period 1990–2017 and 1999–2017. In addition, the study examined how the capital ratio and company specific attributes were used in the process of choosing between distribution strategies.Motivation for the study: The need to determine the impact of the capital ratio within different regions on distribution strategies motivated this study. In addition, the majority of studies on predictors of choice between distribution strategies have ignored the dual and the no distribution policy alternatives relative to share repurchases.Research approach/design and method: all the data used in this research were sourced from the Iress data bases. The research employed an advanced panel threshold regression estimation and a multinomial logistic regression (pooled and fixed effects using the generalised structural equation model).Main findings: Firstly, over the period 1990–2017 the empirical results revealed the existence of a single threshold effect between the debt-to-equity ratio and the dividend payments, and a double threshold effect between the total debt based on the book value and the dividend payment. Secondly, the choice between distribution strategies was driven by company specific attributes.Practical/managerial implication: These findings provide useful insights to South African managers for formulating and maximizing pay-out decisions.Contribution/value-add: The study contributes to the scant body of knowledge on the effect of threshold capital ratio and company specific attributes on distribution strategies.

The green spillover effect of the inward foreign direct investment: Market versus innovation

Little work has focused on whether or under which conditions foreign direct investment (FDI) can spill cleaner-production technologies. To explore the complicated effects of FDI's technical spillover on the green innovation effort of host countries, this paper develops a multi-stage decision-making model in which abatement treatments of firms are described. It is found that marketization level and innovation capacity of the host country are two key factors impacting FDI's green spillovers. The results show there are preconditions in terms of green spillovers, i.e., FDI can improve or has no impact on the environmental performance of local firms under different conditions. Moreover, there are double threshold effects: the degree of FDI's green spillover varies with the different levels of the two threshold variables of marketization and innovation capacity. An increase in innovation capacity always motivates cleaner-technology diffusion in host countries. In addition, a moderate tax system is conducive to the green spillovers of FDI. This paper provides a theoretical support for the pollution halo hypothesis that FDI can promote cleaner development of host countries. Finally, from a perspective of cleaner-production practice, the policy implications are discussed to make production cleaner and ensure environmental sustainability.

Impact of Urbanization and Industrial Structure on Carbon Emissions: Evidence from Huaihe River Eco-Economic Zone

Rapid urbanization in China greatly contributes to carbon emissions, while the industrial structure greatly contributes to changes in the variation of carbon emissions. This research addresses the impact of urbanization and industrial structure on carbon emissions from 2010 to 2018, by focusing on the Huaihe River Eco-economic Zone, which is an important economic corridor along the north–south division of China. Although many studies have focused on investigating the impact of urbanization or industrial structure on carbon emissions, few studies further addressed an analysis of the impact of both on carbon emissions, using multiple measurement models. This paper reveals the holistic and local impact of industrial structure and urbanization on carbon emissions, by integrating a threshold regression model with geographically weighted regression. The results are as follows: (1) From a holistic point of view, industrial structure and urbanization had both, single threshold, and double threshold effects on carbon emissions in the Huaihe River Eco-economic Zone. (2) From a regional perspective, the coefficients of industrial structure on carbon emissions were all positive, but the rate of increase gradually slowed down. The coefficients of urbanization on carbon emissions were all negative, reaching a maximum value of negative effect in 2013. Understanding the holistic and local impact of urbanization and industrial structure on carbon emissions provides governments with differentiated and forward-looking suggestions for mitigating carbon emissions in the Huaihe River Eco-economic Zone.

The Impact of Outward Foreign Direct Investment on Carbon Emission toward China’s Sustainable Development

It is of practical significance to evaluate whether enhancing outward foreign direct investment (OFDI) can promote carbon mitigation under the new urbanization background toward China’s sustainable development and carbon neutrality. The impact of OFDI on carbon emissions is investigated from the dual perspectives of the urbanization threshold and the mediating path by using panel data from China’s 30 provinces during the period of 2003–2015 and considering both population and land. The results show that there is a significant impact from interprovincial OFDI on CO2 emissions with the double threshold effect of urbanization, and that OFDI expansion will increase CO2 emissions with urbanization; however, the different stages of urbanization show inverted U-shaped characteristics that first rise and then fall. The optimization of industrial structures has not passed the mediating effects test during the sample period, while the rationalization of the industrial structure provides a mediating effect in the primary stage of urbanization and a suppressing effect in the high level stage of urbanization. Import dependence only shows a masking effect in the intermediate stage of urbanization, while the technical level shows an intermediary effect in the primary stage of urbanization and a masking effect in the intermediate stage. The intensification of OFDI has brought different effects on economic and social production in various regions of China under urbanization, which has further affected regional carbon emissions. Discussing these effects would help to provide constructive suggestions for the regional coordination of development, new urbanization construction and urban low carbon transformation.

The spatial spillover and threshold effect of green finance onenvironmental quality: evidence from China.

Green finance is a priority for alleviating environmental degradation pressures. Based on panel data of 30 provinces in China from 2009 to 2017, this study utilizes the spatial Durbin model (SDM) and the threshold model to examine the effect of green finance on environmental quality. The empirical results are as follows: (1) Green finance and environmental quality have significant positive spatialautocorrelation; the development of green finance in China is still in the lower-level range, while high-high and low-low are the dominant spatial clustering types. (2) Green finance has a significant positive effect on local environmental quality but has negative effects on that of its neighbors during the research period; however, the spatial spillover effect of green finance is heterogeneous in the three subsamples. (3) There are double threshold effects of green finance on environmental quality, and the effect decreases with the promotion of green finance. These empirical insights provide spatial references for policymakers to formulate strategies for the target of coordinated and integrated regional development.

Emotion Recognition From EEG Signals of Hearing-Impaired People Using Stacking Ensemble Learning Framework Based on a Novel Brain Network

Emotion recognition based on electroencephalography (EEG) signals has become an interesting research topic in the field of neuroscience, psychology, neural engineering, and computer science. However, the existing studies are mainly focused on normal or depression subjects, and few reports on hearing-impaired subjects. In this work, we have collected the EEG signals of 15 hearing-impaired subjects for categorizing three types of emotions (positive, neutral, and negative). To study the differences in functional connectivity between normal and hearing-impaired subjects under different emotional states, a novel brain network stacking ensemble learning framework was proposed. The phase-locking value (PLV) was utilized to calculate the correlation between EEG channels, and then we constructed a brain network using double thresholds. The spatial features of the brain network were extracted from the perspectives of local differentiation and global integration. In addition, the stacking ensemble learning framework was used to classify the fused features. To evaluate the proposed model, extensive experiments were carried out on the SEED dataset, and the result shows that the proposed method achieved superior performance than state-of-the-art models, in which the average classification accuracy is 0.955 (std: 0.052). In addition, the experimental results of hearing-impaired emotion recognition show that the average classification accuracy is 0.984 (std: 0.005). Finally, we investigated the activation patterns to reveal important brain regions and inter-channel relations about emotion recognition.

Threshold Effect of Foreign Direct Investment and Carbon Emissions Performance From the Perspective of Marketization Level: Implications for the Green Economy.

Exploring the path and mechanism of marketization level in the effect of foreign direct investment (FDI) on carbon emission performance will help to maximize the stimulation effect of foreign investment on green and low-carbon development. This study used the panel data of 11 provinces and cities in the Yangtze River Economic Belt from 2008 to 2016. A panel threshold model is constructed to explore the non-linear relationship between FDI and carbon emissions performance from the perspective of marketization level. The main conclusions are as follows: First, from the perspective of marketization level, a significant double threshold effect exists between foreign participation and carbon emission intensity, with threshold values of 4.4701 and 9.2516 respectively. Second, as the marketization level increases, the technology spillover effect of FDI increases, and the stimulation effect of foreign participation on carbon intensity decreases significantly, but it does not inhibit carbon intensity, indicating that the overall benefits brought by FDI technology spillovers are still insufficient to offset pollution caused by foreign investment. Third, the eastern region of the Yangtze River Economic Belt has crossed the second threshold. In the central and western regions, the marketization process is relatively slow except for Chongqing, and the regions are still firmly stuck between the first and second thresholds. In response to the conclusions of the empirical research, relevant policy suggestions are put forward from three dimensions, namely, the strategy of introducing foreign investment, construction of the marketization system, and environmental regulation, to achieve low-carbon and green development in the Yangtze River Economic Belt.

Renewable energy and economic growth: New insight from country risks

This study explored the relationship between renewable energy consumption and economic growth from new risk-based perspectives, including political risks, financial risks, economic risks and composite risks. The study uses a panel threshold model to empirically analyze panel data for the Organization for Economic Cooperation and Development (OECD) countries from 1997 to 2015. The results show when composite risks and political risks are used as threshold variables, there is a single threshold between renewable energy consumption and economic growth. When that threshold is exceeded, the positive effect of renewable energy on economic development increases. When economic risks and financial risks are used as threshold variables, there is a double threshold between renewable energy consumption and economic growth. When the first threshold value is exceeded, but not the second, renewable energy positively impacts economic development. However, when economic risk and financial risk do not lie between the two threshold values, there is an insignificant negative correlation between renewable energy consumption and economic growth.

A step towards sustainable path: The effect of globalization on China’s carbon productivity from panel threshold approach

Surfacing the stress of global CO2 emission reduction and the change into a low-emission economy has become one of the prominent economic concerns in the twenty-first century. The essence of evolving a low-emission economy is to raise carbon productivity that can be estimated as the cost-effective paybacks of CO2 emissions. A panel threshold model was applied to approximate the threshold effect of globalization on carbon productivity under the development of human capital by using the panel data of thirty provinces of China from 2009 to 2017. The empirical findings demonstrate that China's carbon productivity increases, while economic growth shape moves towards sustainable development with low-carbon emission. Moreover, the driving force of globalization on carbon productivity is not tediously decreasing/increasing, but it has a double threshold effect of human capital. In line with this, this study finding found a single and double threshold of 9.3478 and 10.8800, respectively, as a benchmark where the relationship turns positive. The empirical findings have suggested several policy implications for the Chinese Government, policymakers, and regulatory authorities regarding this critical issue.

Spatial Threshold Effect of Industrial Land Use Efficiency on Industrial Carbon Emissions: A Case Study in China.

China’s industry is still in the middle of industrialization. Land use activities are crucial to the growth of carbon emissions. However, few scholars focus on the influence mechanism between industrial land use efficiency (ILUE) and industrial carbon emissions. In this paper, the threshold model and the spatial Durbin model are used to investigate the spatial threshold effect of industrial land use efficiency on industrial carbon emission from 2003 to 2018. The results show that ILUE of China’s provinces basically shows an improvement trend, with little difference in spatial distribution, showing a pattern of high in the eastern region and low in the western region. When economic development level (A) and technical level (T) are taken as the threshold variable, ILUE has a single threshold effect on industrial carbon emissions in the eastern region. In the central region, with a as the threshold variable, ILUE shows a double threshold effect on industrial carbon emission. Under the 0–1 geographical proximity weight matrix, the indirect spillover effect of ILUE on reducing regional carbon emissions is significant, and the indirect effect is even greater than that on regional carbon emissions. The spatial spillover effect is not significant in the eastern region. These findings have important practical significance for promoting regional industrial transformation and upgrading, optimizing land space and realizing high-quality economic development.

Exploring Cherenkov Emission of BGO for TOF-PET

Bismuth germanate (BGO) was the preferred crystal for positron emission tomography (PET) scanners, but was substituted with the emergence of faster crystals. Improvements in silicon photomultipliers (SiPMs) and the use of fast high frequency readout make it possible to use the prompt Cherenkov emission in BGO in order to boost the achievable coincidence time resolution (CTR) significantly. The large fluctuations in the detected Cherenkov photon yield are causing time or amplitude walk effects in the leading edge time discrimination, which are corrected by measuring the initial signal rise time via a double threshold system. Further a classification of “fast” and “slow” timing events is shown to make best use of all the information and upgrades the CTR for most of the 511-keV events. In order to assess the practicability of this novel approach various crystal geometries and state-of-the-art SiPMs from HPK, Ketek, Broadcom, and FBK have been evaluated with the focus on the applicability in total body PET systems. For typical PET sized crystals (3 × 3 × 20 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) coupled to area matching 3 × 3-mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Broadcom SiPMs a time resolution of 261 ± 8 ps full width at half maximum (FWHM) was measured when applying time walk corrections, while the CTR of individual types of events with different Cherenkov yield range from 205 to 302-ps FWHM. A further thorough discussion and prospects of TOF-PET with BGO are given, especially in view of timing event classification of all detected 511-keV events, corresponding to various time of flight kernels ranging from high to low time resolution.

An Improved Speech Base Frequency Extraction Algorithm

Traditional fundamental frequency extraction algorithms mainly include cepstrum method, short-time autocorrelation method. Some end-point extraction methods can’t correctly select the part of effective value due to the particularity of dialect audio and the noise influence. And, some fundamental frequency extraction methods cannot get the data consistent with the trend of tuning values. Against the above, this paper proposes an improved double threshold method for the extraction of the basic frequency according to the characteristics of the vowels of the initials of the one-word tone, which extracts the continuous vowels of the one-word tone, and looks for the basic frequency extraction algorithm that meets the classification of the tones of one-word. Finally, the effective basic frequency data is obtained. This method is more effective than other methods in speech tone recognition. It is of great significance to the study of speech tone, especially the study of dialect tone.

The Montreal Cognitive Assessment (MoCA) with a double threshold: improving the MoCA for triaging patients in need of a neuropsychological assessment.

Diagnosis of patients suspected of mild dementia (MD) is a challenge and patient numbers continue to rise. A short test triaging patients in need of a neuropsychological assessment (NPA) is welcome. The Montreal cognitive assessment (MoCA) has high sensitivity at the original cutoff <26 for MD, but results in too many false-positive (FP) referrals in clinical practice (low specificity). A cutoff that finds all patients at high risk of MD without referring to many patients not (yet) in need of an NPA is needed. A difficulty is who is to be considered at risk, as definitions for disease (e.g. MD) do not always define health at the same time and thereby create subthreshold disorders. In this study, we compared different selection strategies to efficiently identify patients in need of an NPA. Using the MoCA with a double threshold tackles the dilemma of increasing the specificity without decreasing the sensitivity and creates the opportunity to distinguish the clinical (MD) and subclinical (MCI) state and hence to get their appropriate policy. Patients referred to old-age psychiatry suspected of cognitive impairment that could benefit from an NPA (n = 693). The optimal strategy was a two-stage selection process using the MoCA with a double threshold as an add-on after initial assessment. By selecting who is likely to have dementia and should be assessed further (MoCA<21), who should be discharged (≥26), and who's course should be monitored actively as they are at increased risk (21<26). By using two cutoffs, the clinical value of the MoCA improved for triaging. A double-threshold MoCA not only gave the best results; accuracy, PPV, NPV, and reducing FP referrals by 65%, still correctly triaging most MD patients. It also identified most MCIs whose intermediate state justifies active monitoring.

Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network

Background and objectiveModerate exercise contributes to good health. However, excessive exercise may lead to cardiac fatigue, myocardial damage and even exercise sudden death. Monitoring the heart health has important implication to prevent exercise sudden death. Diagnosis methods such as electrocardiogram, echocardiogram, blood pressure and histological analysis have shown that arrhythmia and left ventricular fibrosis are early warning symptoms of exercise sudden death. Heart sounds (HS) can reflect the changes of cardiac valve, cardiac blood flow and myocardial function. Deep learning has drawn wide attention because of its ability to recognize disease. Therefore, a deep learning method combined with HS was proposed to predict exercise sudden death in New Zealand rabbits. The objective is to develop a method to predict exercise sudden death in New Zealand rabbits.MethodsThis paper proposed a method to predict exercise sudden death in New Zealand rabbits based on convolutional neural network (CNN) and gated recurrent unit (GRU). The weight-bearing exhaustive swimming experiment was conducted to obtain the HS of exercise sudden death and surviving New Zealand rabbits (n = 11/10) at four different time points. Then, the improved Viola integral method and double threshold method were employed to segment HS signals. The segmented HS frames at different time points were taken as the input of a combined CNN and GRU called CNN–GRU network to complete the prediction of exercise sudden death.ResultsIn order to evaluate the performance of proposed network, CNN and GRU were used for comparison. When the fourth time point segmented HS frames were taken as input, the result shows that the proposed network has better performance with an accuracy of 89.57%, a sensitivity of 89.38% and a specificity of 92.20%. In addition, the segmented HS frames at different time points were input into CNN–GRU network, and the result shows that with the progress of the experiment, the prediction accuracy of exercise sudden death in New Zealand rabbits increased from 50.98 to 89.57%.ConclusionThe proposed network shows good performance in classifying HS, which proves the feasibility of deep learning in exploring exercise sudden death. Further, it may have important implications in helping humans explore exercise sudden death.

Comparing the Accuracy of Visual and Computerized Onset Detection Methods on Simulated Electromyography Signals with Varying Signal-to-Noise Ratios

Electromyography (EMG) onsets determined by computerized detection methods have been compared against the onsets selected by experts through visual inspection. However, with this type of approach, the true onset remains unknown, making it impossible to determine if computerized detection methods are better than visual detection (VD) as they can only be as good as what the experts select. The use of simulated signals allows for all aspects of the signal to be precisely controlled, including the onset and the signal-to-noise ratio (SNR). This study compared three onset detection methods: approximated generalized likelihood ratio, double threshold (DT), and VD determined by eight trained individuals. The selected onset was compared against the true onset in simulated signals which varied in the SNR from 5 to 40 dB. For signals with 5 dB SNR, the VD method was significantly better, but for SNRs of 20 dB or greater, no differences existed between the VD and DT methods. The DT method is recommended as it can improve objectivity and reduce time of analysis when determining EMG onsets. Even for the best-quality signals (SNR of 40 dB), all the detection methods were off by 15–30 ms from the true onset and became progressively more inaccurate as the SNR decreased. Therefore, although all the detection methods provided similar results, they can be off by 50–80 ms from the true onset as the SNR decreases to 10 dB. Caution must be used when interpreting EMG onsets, especially on signals where the SNR is low or not reported at all.

Low walk error multi-stage cascade comparator for TOF LiDAR application

Aiming at the application of pulsed TOF LiDAR, this paper propose a low walk error multi-stage comparator based on the double threshold method. In order to achieve high-speed comparison output, the LVDS interface is used to output the data of the comparator. The dual-channel comparator is implemented using the SMIC 0.18 μm process which achieves a propagation delay of 620 ps and an input bandwidth of 256 MHz. Thanks to the multi-stage design, the walk error of the comparator is only 196 ps. It supports the minimum pulse width of 931 ps, the rms random jitter of 1.90 ps, and the power consumption of 39.6 mW.

Thresholding Functional Connectivity Matrices to Recover the Topological Properties of Large-Scale Neuronal Networks.

The identification of the organization principles on the basis of the brain connectivity can be performed in terms of structural (i.e., morphological), functional (i.e., statistical), or effective (i.e., causal) connectivity. If structural connectivity is based on the detection of the morphological (synaptically mediated) links among neurons, functional and effective relationships derive from the recording of the patterns of electrophysiological activity (e.g., spikes, local field potentials). Correlation or information theory-based algorithms are typical routes pursued to find statistical dependencies and to build a functional connectivity matrix. As long as the matrix collects the possible associations among the network nodes, each interaction between the neuron i and j is different from zero, even though there was no morphological, statistical or causal connection between them. Hence, it becomes essential to find and identify only the significant functional connections that are predictive of the structural ones. For this reason, a robust, fast, and automatized procedure should be implemented to discard the “noisy” connections. In this work, we present a Double Threshold (DDT) algorithm based on the definition of two statistical thresholds. The main goal is not to lose weak but significant links, whose arbitrary exclusion could generate functional networks with a too small number of connections and altered topological properties. The algorithm allows overcoming the limits of the simplest threshold-based methods in terms of precision and guaranteeing excellent computational performances compared to shuffling-based approaches. The presented DDT algorithm was compared with other methods proposed in the literature by using a benchmarking procedure based on synthetic data coming from the simulations of large-scale neuronal networks with different structural topologies.