IQ Data Research Articles

LIFETIME EXPOSURES AND COGNITION AT OLDER AGES: FINDINGS FROM THE WISCONSIN LONGITUDINAL STUDY

Abstract This symposium considers the relationship between later-life cognition and a range of social, economic, and environmental exposures throughout the life course. Drawing on over 65 years of rich population-based survey data and recent rigorous cognitive assessments from the Wisconsin Longitudinal Study (WLS), the papers in this symposium showcase empirical linkages between older adults’ cognitive health with a set of exposures including education, family socioeconomic status, occupational conditions, and familial connections. The first paper (Herd et al.) draws on the unique availability of adolescent IQ and high caliber cognitive diagnostic data that distinguishes between underlying etiologies to examine the relationship between early life IQ, educational attainment, and later life dementia. The paper by Topping links the wealth of WLS data on early life socioeconomic background and high school experiences to patterns of change in later life cognition, with attention to both cognitive change and mechanisms of selection. Next, Qin and Engelman draw on the WLS’s sibling data to consider the relationship between later-life cognitive health and educational differentials among siblings, and Lee et al. consider the impact of sibling relationships on later life cognition. Finally, Williams et al. draw on the WLS survey, genetic, and cognitive data to consider the interaction between ApoE-4 status, employment-based environmental exposures in midlife, and later life pollution in shaping dementia risk. Taken together, the papers in this symposium showcase the enormous potential of combining longitudinal survey data with cognitive assessment for learning about the life course determinants of later-life cognitive health.

Airborne Lead Exposure and Childhood Cognition: The Environmental Influences on Child Health Outcomes (ECHO) Cohort (2003-2022).

Objectives. To examine whether a previously reported association between airborne lead exposure and children's cognitive function replicates across a geographically diverse sample of the United States. Methods. Residential addresses of children (< 5 years) were spatially joined to the Risk-Screening Environmental Indicators model of relative airborne lead toxicity. Cognitive outcomes for children younger than 8 years were available for 1629 children with IQ data and 1476 with measures of executive function (EF; inhibitory control, cognitive flexibility). We used generalized linear models using generalized estimating equations to examine the associations of lead, scaled by interquartile range (IQR), accounting for individual- and area-level confounders. Results. An IQR increase in airborne lead was associated with a 0.74-point lower mean IQ score (b = -0.74; 95% confidence interval = -1.00, -0.48). The association between lead and EF was nonlinear and was modeled with a knot at the 97.5th percentile of lead in our sample. Lead was significantly associated with lower mean inhibitory control but not with cognitive flexibility. This effect was stronger among males for both IQ and inhibitory control. Conclusions. Early-life exposure to airborne lead is associated with lower cognitive functioning. (Am J Public Health. 2024;114(3):309-318. https://doi.org/10.2105/AJPH.2023.307519).

Meta-analysis: on average, undergraduate students’ intelligence is merely average

Background: According to a widespread belief, the average IQ of university students is 115 to 130 IQ points, that is, substantially higher than the average IQ of the general population (M = 100, SD = 15). We traced the origin of this belief to obsolete intelligence data collected in 1940s and 1950s when university education was the privilege of a few. Examination of more recent IQ data indicate that IQ of university students and university graduates dropped to the average of the general population. The decline in students’ IQ is a necessary consequence of increasing educational attainment over the last 80 years. Today, graduating from university is more common than completing high school in the 1940s. Method: We conducted a meta-analysis of the mean IQ scores of college and university students samples tested with Wechsler Adult Intelligence Scale between 1939 and 2022. Results: The results show that the average IQ of undergraduate students today is a mere 102 IQ points and declined by approximately 0.2 IQ points per year. The students’ IQ also varies substantially across universities and is correlated with the selectivity of universities (measured by average SAT scores of admitted students). Discussion: These findings have wide-ranging implications. First, universities and professors need to realize that students are no longer extraordinary but merely average, and have to adjust curricula and academic standards. Second, employers can no longer rely on applicants with university degrees to be more capable or smarter than those without degrees. Third, students need to realize that acceptance into university is no longer an invitation to join an elite group. Fourth, the myth of brilliant undergraduate students in scientific and popular literature needs to be dispelled. Fifth, estimating premorbid IQ based on educational attainment is vastly inaccurate, obsolete, not evidence based, and mere wishful thinking. Sixth, obsolete IQ data or tests ought not to be used to make high-stakes decisions about individuals, for example, by clinical psychologists to opine about the intelligence and cognitive abilities of their clients.

Who should select the external auditor in emerging economies? Role of institutional ownership and family ownership

Aim/purpose – This study reports the demand for Big 4 audits among institutional and family owners, the two dominant ownerships in the GCC countries. We conducted this in-depth study to gain an understanding of the type of firms, family-owned or institu- tional-owned firms that lead to choosing audit firms. Design/methodology/approach – This study employed a quantitative cross-country study by selecting a sample based on secondary data extracted from the Capital IQ data- -set from a panel of 1827 non-financial firms listed on the stock exchanges of the Gulf Cooperation Council (GCC) countries from 2010 to 2018. The hypothesized effects of institutional ownership (IO) and family ownership (FO) on the selection of external auditors in these countries were examined using logit, probit, and heteroskedastic probit analysis. Findings – The study finds that institutional investors play a crucial role in influencing firms’ choice of auditors in the GCC. Family-owned firms tend to hire non-Big 4 firms when the owners actively monitor the firms’ financial transactions. In addition, the study finds that both domestic and foreign institutional investors have a significant positive effect on auditor selection, with domestic institutional investors having priority. These findings support the efforts of market authorities in the GCC to highlight the critical role of IO over FO in improving audit quality. Research implications/limitations – The results are highly relevant for shareholders, executives, institutional investors, regulators, and academics. They help them improve the growth of capital and audit markets by developing best practices, thereby helping achieve an optimal framework for auditor choice that matches higher audit quality. This study focuses on only two types of ownership structures (institution and family) despite the many options because of the extensive debates and discussions on the association between the studied ownership types and auditor choice. Originality/value/contribution – Study highlighted the role of institutional investors in GCC countries as one of the most attractive emerging economies in the Middle East. Since no research has been conducted on the role of institutional and family investors in selecting external auditors in GCC countries, this study has made a significant contribu- tion to the accounting and auditing literature. It mitigates the gap in the literature on emerging markets. The findings can provide policymakers with guidelines for including institutional investors and FO in GCC countries to ensure high-quality audits. Keywords: institutional ownership (IO), family ownership (FO), auditor choice, Gulf Cooperation Council, emerging economies. JEL Classification: G23, G32, D10, M42.

Equity ownership structure and internationalization of firms: the role of international experience of board chairs

PurposeThis study aims to investigate the impact of equity ownership structure (i.e. CEO ownership, board chair ownership and institutional ownership) on internationalization of firms. The moderating role of international experience of board chairs is also examined.Design/methodology/approachThis study uses Compustat-Capital IQ data from Standard &Poor’s. The sample of this study includes 309 US multinational corporations representing different sectors. The parameters were estimated by using the ordinary least squares regression with the SPSS statistical package.FindingsThe finding of this study suggests that CEO ownership and board chair ownership have a significant, positive impact on the degree of internationalization of firms, whereas institutional ownership has a negative impact. The predicted moderating role of international experience of board chairs has found mixed results.Originality/valueThis study contributes to the literature by taking a holistic approach to examine the impact of equity ownership types (i.e. CEO ownership, board chair ownership and institutional ownership) on firms’ degree of internationalization. To the best of the authors’ knowledge, this research is also the first to investigate the impact of independent board chairs’ equity ownership and international experience on internationalization.

Kidney stone twinkling power in elevated oxygen, carbon dioxide, and nitrogen environments

Clinically, ∼66% of kidney stones exhibit the Doppler ultrasound twinkling artifact. Previous studies show breathing pure oxygen at elevated pressures increases twinkling compared to ambient air. Here, we investigate how different gases influence twinkling on ex vivo and in vivo human kidney stones. 38 kidney stones were imaged with a research ultrasound system and ATL L7-4 transducer in elevated oxygen, carbon dioxide, and nitrogen environments. In vivo stones were imaged with the same ultrasound system and ATL C5-2 transducer in 25 human subjects before and after breathing pure oxygen for 15 min. IQ data were processed by calculating the average Doppler power, or twinkling, for a region of interest containing the stone. In the lab, increasing oxygen by 55%, carbon dioxide by 100%, and nitrogen by 17% increased twinkling power an average of 70 ± 28%, 137 ± 53%, and 79 ± 31%, respectively. In human subjects, breathing oxygen increased twinkling power an average of 27 ± 58%, with seven subjects showing a strong increase (&amp;gt;30%); stone composition and patient age influenced twinkling in patients breathing oxygen. These results suggest that varying ambient gases may be leveraged to increase the consistency of twinkling and improve kidney stone detection with ultrasound. [Work supported by the PSU Center for Biodevices.]

The associations among executive planning, self-determination, and quality of life in adolescents with intellectual disability.

Skills such as planning and problem solving that are required in self-determination can be cognitively demanding. It has not yet been examined whether executive functions and intelligence are associated with levels of self-determination in individuals with intellectual disability (ID), and how that is related to quality of life (QoL). This study examined the associations among executive functions, intelligence, self-determination, and QoL in adolescents with ID. Seventy-nine adolescents aged between 17 and 20years with mild ID participated in the study. Executive functions were assessed by experimental tasks. Non-verbal IQ and survey data regarding QoL and self-determination capacity were collected from the participants. In a regression model with QoL as the dependent variable, only executive planning and self-determination capacity (but not working memory, inhibition and IQ) were significant predictors of QoL. Two mediation models were tested based on the hypotheses, literature and current findings. Model 1 revealed that executive planning had a negative direct effect on QoL when the mediator, self-determination capacity, was held constant. Model 2 indicated that the significant association between self-determination and QoL was not mediated by executive planning. The findings highlighted the crucial significance of self-determination, in comparison with executive functions and intelligence, for improving the QoL in adolescents with ID. Importantly, higher executive planning skill was even associated with lower QoL if self-determination was not concurrently strengthened. These findings carry implications for the design of education and intervention programmes aimed at improving QoL of adolescents with ID.

A lightweight multi-feature fusion structure for automatic modulation classification

Automatic modulation classification (AMC) is a useful technology for Internet of Things devices to automatically distinguish the modulation scheme of the received signal. Recently, deep learning methods have been a popular solution for AMC, but these methods generate unsatisfactorily high model complexity while achieving acceptable classification accuracy. Therefore, developing an AMC method that can balance classification accuracy and model complexity is a challenging task, especially when targeting resource-constrained devices. In this paper, we propose a lightweight multi-feature fusion structure (lightMFFS) for AMC. This structure consists of three parts, namely data processing, feature extraction, and classification. Specifically, in the data processing part, we adopt three types of signal data, including IQ data, AP data, and IQ-transformed data. In the feature extraction part, we design three lightweight extractors to extract different spatial features of the signal from different data. In the classification part, an attention fuser is used to integrate all the features and enhance the important channel features to provide comprehensive and effective signal knowledge for classification. Extensive comparative experiments with current popular models show that our method achieves higher classification accuracy with fewer training parameters and has a more stable and superior learning capability.

Patient reported outcomes do not correlate to functional knee recovery and range of motion in total knee arthroplasty

BackgroundMany total knee arthroplasty (TKA) patients exhibit continued pain and limited function following surgery. Determining TKA outcomes is typically reliant on post-operative evaluations and completing patient-reported outcomes (PROMs). Due to low compliance rates, it is essential to identify new strategies for monitoring patients. The purpose of this analysis was to assess the correlations between gait kinematics, PROMs, and knee range of motion (ROM). Methods130 patients (75 female) received Persona IQ TKA (Zimmer Biomet, Warsaw, IN, USA) which includes a stem extension with embedded accelerometer and gyroscope. PROM scores were compared at baseline and 6 weeks post-TKA using a paired t-test. Gait kinematics were recorded daily via the Persona IQ stem extension. Pearson's correlation coefficients were derived between PROMs and average gait kinematics. ResultsKnee Injury and Osteoarthritis Outcome Score (KOOS Jr.) and Veterans RAND 12 (VR-12) physical scores improved following surgery (p ≤ 0.001, p = 0.003, respectively). Weak statistically significant correlations were found between PROMS and gait kinematics. ConclusionWeak correlations between PROMs and gait kinematics indicate patient perception of improvement and objectively measured functional status may not be interchangeable. Further, compliance with Persona IQ data reached 95.4–97.7% (depending on the parameter) at 6 weeks following surgery, a 20% higher compliance rate over PROMs. Daily functional measurements provide insight into the patient's progression and may be useful in detecting poor outcomes.

Toward Next-Generation Signal Intelligence: A Hybrid Knowledge and Data-Driven Deep Learning Framework for Radio Signal Classification

Automatic modulation classification (AMC) can generally be divided into knowledge-based methods and data-driven methods. In this paper, we explore combining the knowledge-based method and data-driven technology to take full advantage of both and propose a hybrid knowledge and data-driven deep learning framework (HKDD) for AMC. To make the handcrafted features more discriminative, various traditional features are adopted, including instantaneous features, statistical features, and spectral features. In the HKDD framework, a feature fusion mechanism is proposed to integrate the features learned from the original signal with those processed by a fully connected network from the handcrafted features. Besides, an attention mechanism is implemented on the fused features to neglect immature features and highlight important features. To evaluate the performance of the proposed method, we construct two modulation classification datasets containing both traditional features and raw IQ data. The bigger one contains 36 modulation categories, which is greater than the number of categories of any AMC dataset currently available. Simulation results show that our proposed method has significant performance gain in both adequate-sample classification scenario and few-shot classification scenario.

Prenatal and current phthalate exposure and cognitive development in 7-year-old children from the Odense child cohort

BackgroundPhthalates are endocrine disrupters used in a variety of consumer products. Human studies suggest an association between phthalate exposure and cognitive development but adverse effects of the recently introduced phthalate substitutes have only been sparsely studied. ObjectivesTo investigate associations between prenatal and concurrent exposure to phthalates and IQ in 7-year-old children from the Odense Child Cohort. MethodsPregnant women from the Odense Child Cohort had phthalate metabolites measured in urine samples during 3rd trimester in 2010 to 2012. In addition, phthalates were also measured in urine samples from their offspring at age 7 years. IQ was assessed at age 7 years using four subtests from Wechsler Intelligence Scale for Children. The total study population consisted of 585 mother-child pairs with available prenatal urinary phthalate metabolites concentrations and IQ data at age 7 years. A subset of those (N = 274) had urinary phthalate metabolites measured in child urine at age 7 years. Phthalate concentrations were grouped into tertiles and associations with IQ were investigated using multiple linear regression adjusting for sex, maternal education and maternal/child BMI. ResultsUrinary phthalate metabolite concentrations both in pregnant women and children were generally lower compared to previous cohorts. Children with high prenatal urinary concentrations of MEP and metabolites of DEHP (∑DEHPm)(3rd tertile) had −3.1 (95% CI: −5.5, −0.6) (MEP) and − 3.0 (−5.5, −0.6) (∑DEHPm) IQ points at age 7 years compared to children with low concentrations (1st tertile). High concurrent urinary phthalate concentrations of MCPP, ∑DnHxPm, ∑DiDPm and ∑DiNPm in the 3rd tertile was associated with −3.7 (−7.2, −0.2), −4.4 (−7.9, −0.9), −3.7 (−7.2, −0.2) and − 5.6 (−9.1, −2.2) IQ points, respectively, compared to those with the lowest concentrations (1st tertile). ConclusionWe found significant inverse associations between some prenatal and concurrent urinary phthalate concentrations and IQ at age 7 years in this low exposed population. This suggests that exposure to phthalates both prenatally and during early childhood could be hazardous to child neurodevelopment, however, large-scale prospective studies assessing phthalate exposure through multiple urine samples, and possibly investigating cocktail effects of the chemicals as well as long term follow-up are warranted.

IQ Modulates Coupling Between Diverse Dimensions of Psychopathology in Children and Adolescents.

Correlations between cognitive ability and psychopathology are well recognized, but prior research has been limited by focusing on individuals with intellectual disability, single-diagnosis psychiatric populations, or few measures of psychopathology. Here, we quantify relationships between full-scale IQ and multiple dimensions of psychopathology in a diverse care-seeking population, with a novel focus on differential coupling between psychopathology dimensions as a function of IQ. A total of 70 dimensional measures of psychopathology, plus IQ and demographic data, were collated for 2,752 children and adolescents from the Healthy Brain Network dataset. We first examined univariate associations between IQ and psychopathology, and then characterized how the correlational architecture of psychopathology differs between groups at extremes of the IQ distribution. Associations with IQ vary in magnitude between different domains of psychopathology: IQ shows the strongest negative correlations with attentional and social impairments, but is largely unrelated to affective symptoms and psychopathy. Lower IQ is associated with stronger coupling between internalizing problems and aggression, repetitive behaviors, and hyperactivity/inattentiveness. Our analyses reveal that variation in general cognitive ability is associated not only with significant and selective shifts in severity of psychopathology, but also in the coupling between different dimensions of psychopathology. These findings have relevance for the clinical assessment of mental health in populations with varying IQ, and may also inform ongoing efforts to improve the measurement of psychopathology and to understand how relationships between cognition and behavior are reflected in brain organization. We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure sex balance in the selection of non-human subjects. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper received support from a program designed to increase minority representation in science. We actively worked to promote sex and gender balance in our author group. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. While citing references scientifically relevant for this work, we also actively worked to promote sex and gender balance in our reference list. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science.

METHOD OF DETERMINING PARAMETERS OF MODULATION OF UAV SIGNALS USING ARTIFICIAL NEURAL NETWORKS

The article is devoted to the consideration of the issue of determining the parameters of the modulation of signals of unmanned aerial vehicles using artificial neural networks by recognizing the types of digital modulation and is performed by a system that automatically classifies the type of digital modulation of the received signal. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recognition is carried out the following types of digital modulation: 2-PSK, 4-PSK, 8-PSK, 2-FSK, 8-QAM, 16-QAM, 64-QAM and OFDM. This article uses a neural network approach based on cumulative characteristics. To solve these problems, the methods of calculation and selection of informative cumulative features described in the developed model of the system of automatic recognition of digital modulation types by means of a neural network on cumulative features at a known value of carrier frequency are described. A model of the system of automatic recognition of digital modulation types at a known value is constructed carrier frequency. An algorithm for automatic recognition of digital modulation types has been developed using a multilayer neural network. The influence of noise in the communication channel on the probability of recognizing the types of digital modulation at a known value of the carrier frequency is investigated. It was found that regardless of the type of noise in the communication channel, the law of error distribution in IQ data becomes close to normal. This fact is one important argument for the use of cumulative features in the task of automatic recognition of types of digital modulation. Therefore, the task of automatic recognition of digital modulation types is quite relevant. Further research may be aimed at expanding the range of high-order cumulative features used, due to which it is possible to increase the probability of correct recognition of types of digital modulation, and solving the recognition problem at an unknown value of the frequency and initial phase of the carrier signal.

The Doppler ultrasound twinkling artifact on crevices etched in silicon wafers

The twinkling artifact, or rapid color shifts, are observed when imaging kidney stones and other mineralizations. The current theory is that scattering from bubbles in micron-sized crevices causes twinkling. Our objective is to investigate the effect of ultrasound frequency and crevice size and number on twinkling of silicon wafers. Ten or 100 randomly positioned cylindrical crevices with diameters of 1, 10, or 100 μm and depths of 10 μm were dry-etched on wafers. A research ultrasound system was used to image wafers at 5, 7.8, and 18.5 MHz; IQ data were post-processed to calculate twinkling or Doppler power. Twinkling for all crevice sizes and numbers followed the trend 5 MHz&amp;gt;7.8 MHz&amp;gt;18.5 MHz. For crevice size, at 5 and 7.8 MHz, twinkling was higher for 1- or 10-μm crevices compared to 100 μm; at 18.5 MHz, twinkling was only observed on 100-μm crevices. Increasing the number of crevices increased twinkling for 1 μm crevices at 5 and 7.8 MHz and 10 μm crevices at 7.8 MHz; no differences were noted at 18.5 MHz. These results support previous work showing twinkling is frequency-dependent and suggest that even 10, 1-μm crevices are sufficient to cause twinkling. [Work supported by SURIEA, NSF CAREER-1943937.]

ANALISIS POTENSI DIRI MAHASISWA TERHADAP MINAT MENJADI GURU MATEMATIKA

The purpose of this study is to analyze and describe students' self-potential from the potential sources of IQ and potential EQ for interest in becoming mathematics teachers. The method used in this study is a qualitative descriptive method with the subject of this research being students majoring in mathematics education who collect 87 students. The technIQue of collecting data using tests and filling out questionnaires. The test is used to obtain student IQ data, while filling out the questionnaire sheet is used to obtain EQ data and students' interest in becoming a teacher. Data acquisition through tests and questionnaire filling is done online by using the help of Google Form media. Based on the results of data processing there are still students who are lacking in terms of IQ, but students have a good EQ with considerable interest in the teaching profession. Good student EQ through self-awareness contributed 60%, independent students contributed 51%, student motivation contributed 92%, student empathy contributed 89%, and students' social skills contributed 69%. Besides that students' interest to become teachers is approved through awareness, emotions, and conation. Student cognition contributed 94%, student logistics contributed 93%, and contribution or contribution 62%. Thus it can be concluded that mathematics education students are quite ready to become a teacher

Literacy Versus Ethnicity as a Proxy for National IQ

This study converts literacy rates into IQ scores based on their relationship in countries for which both measures are available; and, validates the same for countries for which direct measurements of National IQ are not available. Two tests are performed: the first for the period 2009-2012 (using Lynn and Meisenberg’s IQ data), and the second for the period 2013-2016 (using Altinok, Angrist and Patrinos’ Scholastic Achievement data). In both tests, the literacy rate-based IQ scores outperform Lynn and Vanhanen’s ethnicity-based interpolations of National IQ in explaining GDP per capita. Indeed, the literacy-based IQ scores reduce the explanatory power of the ethnicity-based IQ scores to marginal significance. These results question the continued use of ethnicity-based IQ data in transnational studies.

The Performance Analysis of Complex-Valued Neural Network in Radio Signal Recognition

Many techniques have been developed for wireless signal recognition in many fifth generation (5G) enabled derivatives. Many harsh constraints, such as the large amount of model parameters and complex signal characteristics, drives intelligent recognition method in real-world settings. In this paper, we propose a generalizable, practical method for raw IQ signal recognition. Specifically, deep complex-valued convolutional neural network models, including a Complex-valued Visual Geometry Group (VGG) (CxVGG) model and a Complex-valued ResNet (CxRN) model, are proposed for handling raw signal IQ data. We examine the merit of complex-valued neural networks (CxNN) and validate their performance with experiments using two public datasets. With an SNR of 10dB, the proposed algorithm achieves a recognition accuracy of 96% on the RadioML2018.10a dataset. When performing drone recognition, CvNN can achieve a recognition accuracy of 99%. Our experimental results verify that deep complex-valued neural network models can achieve considerably improved accuracy with lower computation complexity and fewer model parameters than their real-valued counterparts.

Radiometric Identification of Signals by Matched Whitening Transform

Radiometric identification is the problem of attributing a signal to a specific source. In this work, a radiometric identification algorithm is developed using the whitening transformation. The approach stands out from the more established methods in that it works directly on the raw IQ data and hence is featureless. As such, the commonly used dimensionality reduction algorithms do not apply. The premise of the idea is that a data set is “most white” when projected on its own whitening matrix than on any other. In practice, transformed data are never strictly white since the training and the test data differ. The Förstner-Moonen measure that quantifies the similarity of covariance matrices is used to establish the degree of whiteness. The whitening transform that produces a data set with the minimum Förstner-Moonen distance to a white noise process is the source signal. The source is determined by the output of the mode function operated on the Majority Vote Classifier decisions. Using the Förstner-Moonen measure presents a different perspective compared to maximum likelihood and Euclidean distance metrics. The whitening transform is also contrasted with the more recent deep learning approaches that are still dependent on feature vectors with large dimensions and lengthy training phases. It is shown that the proposed method is simpler to implement, requires no features vectors, needs minimal training and because of its non-iterative structure is faster than existing approaches.

Accuracy Analysis of Feature-Based Automatic Modulation Classification via Deep Neural Network.

A feature-based automatic modulation classification (FB-AMC) algorithm has been widely investigated because of its better performance and lower complexity. In this study, a deep learning model was designed to analyze the classification performance of FB-AMC among the most commonly used features, including higher-order cumulants (HOC), features-based fuzzy c-means clustering (FCM), grid-like constellation diagram (GCD), cumulative distribution function (CDF), and raw IQ data. A novel end-to-end modulation classifier based on deep learning, named CCT classifier, which can automatically identify unknown modulation schemes from extracted features using a general architecture, was proposed. Features except GCD are first converted into two-dimensional representations. Then, each feature is fed into the CCT classifier for modulation classification. In addition, Gaussian channel, phase offset, frequency offset, non-Gaussian channel, and flat-fading channel are also introduced to compare the performance of different features. Additionally, transfer learning is introduced to reduce training time. Experimental results showed that the features HOC, raw IQ data, and GCD obtained better classification performance than CDF and FCM under Gaussian channel, while CDF and FCM were less sensitive to the given phase offset and frequency offset. Moreover, CDF was an effective feature for AMC under non-Gaussian and flat-fading channels, and the raw IQ data can be applied to different channels’ conditions. Finally, it showed that compared with the existing CNN and K-S classifiers, the proposed CCT classifier significantly improved the classification performance for MQAM at N = 512, reaching about 3.2% and 2.1% under Gaussian channel, respectively.

STUDY NEUR NETWORKS FOR SOFTWARE DEFINED RADIO CONTROL

The scientific article is devoted to the issues of SDR system control. Software Defined Radio is a system designed for software control of information transmission processes in a radio communication channel. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recognition is carried out the following types of digital modulation: 2-PSK, 4-PSK, 8-PSK, 2-FSK, 8-QAM, 16-QAM, 64-QAM and OFDM. This article uses a neural network approach based on cumulative characteristics. To solve these problems, the methods of calculation and selection of informative cumulative features described in the developed model of the system of automatic recognition of digital modulation types by means of a neural network on cumulative features at a known value of carrier frequency are described. A model of the system of automatic recognition of digital modulation types at a known value is constructed carrier frequency. An algorithm for automatic recognition of digital modulation types has been developed using a multilayer neural network. The influence of noise in the communication channel on the probability of recognizing the types of digital modulation at a known value of the carrier frequency is investigated. It was found that regardless of the type of noise in the communication channel, the law of error distribution in IQ data becomes close to normal. This fact is one important argument for the use of cumulative features in the task of automatic recognition of types of digital modulation. Therefore, the task of automatic recognition of digital modulation types is quite relevant. Further research may be aimed at expanding the range of high-order cumulative features used, due to which it is possible to increase the probability of correct recognition of types of digital modulation, and solving the recognition problem at an unknown value of the frequency and initial phase of the carrier signal.