Handheld Spectrum Analyzer

The lack of user-friendly analyzers has made it difficult for musicians and other sound-based professions to reliably optimize the clarity of their performance. Despite the availability of digital sound conversion and optimization solutions in the market, only a handful of devices can detect the sources of interference anywhere. It is also important to ensure that sound quality is evenly distributed and perceived to be acceptable throughout the venue. The measured power distribution over various frequency ranges can be used to tune and arrange musical equipment to optimize the sound quality experienced by the audience. The Handheld Spectrum Analyzer is a device that analyzes the spectrum of input sound sources received from either audio equipment or the surrounding environment. In addition, the device allows the user to move throughout the working area and observe the different power distributions of the sound spectrum. The proof-of-concept prototype for our Handheld Spectrum Analyzer should be able to accept surrounding sound as input, calculate its frequency spectrum and display its corresponding power bar graphs on the display unit. Furthermore, the device’s interface will tentatively have other features, such as allowing the user to save and store captured spectrums for future comparisons.&nbsp

Digital health interventions for delivery of mental health care: systematic and comprehensive meta-review.

BACKGROUND: The COVID-19 pandemic has shifted mental health care delivery to digital platforms, videoconferencing, and other mobile communications. However, existing reviews of digital health interventions are narrow in scope and focus on a limited number of mental health conditions. OBJECTIVE: To address this gap, we conducted a comprehensive systematic meta-review of the literature to assess the state of digital health interventions for the treatment of mental health conditions. We searched MEDLINE for secondary literature published between 2010 and 2021 on the use, efficacy, and appropriateness of digital health interventions for the delivery of mental health care. RESULTS: Of the 3022 records identified, 466 proceeded to full-text review and 304 met the criteria for inclusion in this study. A majority (52%) of research involved the treatment of substance use disorders, 29% focused on mood, anxiety, and traumatic stress disorders, and >5% for each remaining mental health conditions. Synchronous and asynchronous communication, computerized therapy, and cognitive training appear to be effective but require further examination in understudied mental health conditions. Similarly, virtual reality, mobile apps, social media platforms, and web-based forums are novel technologies that have the potential to improve mental health but require higher quality evidence. CONCLUSIONS: Digital health interventions offer promise in the treatment of mental health conditions. In the context of the COVID-19 pandemic, digital health interventions provide a safer alternative to face-to-face treatment. However, further research on the applications of digital interventions in understudied mental health conditions is needed. Additionally, evidence is needed on the effectiveness and appropriateness of digital health tools for patients who are marginalized and may lack access to digital health interventions