We report here on the utilization of signal in-phase-quadrature (I-Q) diagrams in a novel modulation classification (MC) technique. This MC technique is able to classify linear digital single-carrier modulations as well as multi-carrier modulations. The method uses the waveforms' I-Q diagrams and, by employing a combination of k-center and k-means algorithms, determines the type of modulation. Implementation and refinement of the novel single-carrier modulation classification technique using the I-Q diagrams are discussed in detail. Further, a model for classification of multi-carrier signals is presented, including Gaussianity, cyclostationarity, and autocorrelation tests for further extracting orthogonal frequency division multiplexing signal parameters. Finally, results of this method are presented and compared to other classification methods, and the considerations for implementing the method in hardware are briefly discussed. As a future direction of this research, the performance of the algorithm in fading channels is an interesting topic to pursue.
and Professional Programs. His research interests include software-defined radio and cognitive radio. Okhtay Azarmanesh, Pennsylvania State University OKHTAY AZARMANESH is a Ph.D. candidate in Electrical Engineering at Penn State. He received his B.Sc. in Electrical Engineering from Sharif University of Technology and his M.Sc. from Télécom Paris and SUPAERO. His research interests include software-defined radios, modulation classification in cognitive radios, wireless communications, and satellite communications.
The past few decades saw considerable advances in research and dissemination of evidence-based psychotherapies, yet available treatment resources are not able to meet the high need for care for individuals suffering from depression or anxiety. Blended care psychotherapy, which combines the strengths of therapist-led and internet interventions, can narrow this gap and be clinically effective and efficient, but has rarely been evaluated outside of controlled research settings. This study evaluated the effectiveness of a blended care intervention (video-based cognitive behavior therapy and internet intervention) under real-world conditions. This is a pragmatic retrospective cohort analysis of 385 participants with clinical range depression and/or anxiety symptoms at baseline, measured using Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7), who enrolled in blended care psychotherapy treatment. Participants resided in the United States and had access to the blended care intervention as a mental health benefit offered through their employers. Levels of depression and anxiety were tracked throughout treatment. Hierarchical linear modeling was used to examine the change in symptoms over time. The effects of age, gender, and providers on participants’ symptom change trajectories were also evaluated. Paired sample t-tests were also conducted, and rates of positive clinical change and clinically significant improvement were calculated. The average depression and anxiety symptoms at 6 weeks after the start of treatment were 5.94 and 6.57, respectively. There were significant linear effects of time on both symptoms of depression and anxiety (β=–.49, <i>P</i><.001 and β=–.64, <i>P</i><.001). The quadratic effect was also significant for both symptoms of depression and anxiety (β=.04, P<.001 for both), suggesting a decelerated decrease in symptoms over time. Approximately 73% (n=283) of all 385 participants demonstrated reliable improvement, and 83% (n=319) recovered on either the PHQ-9 or GAD-7 measures. Large effect sizes were observed on both symptoms of depression (Cohen d=1.08) and of anxiety (d=1.33). Video blended care cognitive behavioral therapy interventions can be effective and efficient in treating symptoms of depression and anxiety in real-world conditions. Future research should investigate the differential and interactive contribution of the therapist-led and digital components of care to patient outcomes to optimize care.
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