The speaker will present frameworks and systematic approaches to plan an R&D project portfolio that aligns with the overall business strategy of your organization. Because R&D funds and engineers are limited resources ensuring you are making well founded resource allocations to the right projects will contribute to the success of your organizations.

Artificial Intelligence (AI) and Machine Learning are fast permeating every aspect of the modern world. Nowhere is this more evident than in the range, scope and penetration of AI into the consumer electronics market. From smartphones and mobile devices through distributed/wearable sensors and smart home appliances, AI has revolutionized the consumer electronics marketplace. However, in academia, AI programming and development is usually introduced to students at postgraduate level e.g. Masters Degrees, etc. This tutorials considers some ways and means of introducing the concepts and importance of AI to electronic/computer engineering students as part of their syllabus. What are the benefits and challenges of how this might be realized, what impact might this have for the industrial companies hiring graduates and what format might such initiatives take? These and other questions will be addressed over the course of this tutorial.

Electronic products today require consumer insights to improve the product quality, use and applications. These insights are important for both businesses as well as consumers to improve the reach and usefulness. The increased use of AI and data can help unravel a wide range of market insights to improve the products. The scenario before and after the pandemic has changed the consumer perspectives and product choices. The speakers will present the importance of consumer analytics in the changing times we live in today through a variety of real-life examples. The tutorial will also discuss various aspects of AI, including hardware solutions and how general intelligence will play a role in understanding consumer behavior and give insights in the future.

Voice is an important means for human beings to communicate with each other and integrate their minds into society. Advanced technology has also sparked a variety of voice-related applications for the consumer electronics (CE) market. An important measure of the effectiveness of these applications is the quality, intelligibility and automatic recognition accuracy of the processed speech signals provided by these applications. Therefore, a number of speech evaluation metrics have been derived to quantitatively measure specific properties of speech signals. These metrics are crucial for the development of speech-related application systems. Direct evaluation methods measure the difference between distorted/processed speech and a clean reference at the signal level. Notable examples include speech distortion index (SDI), signal-to-noise-ratio (SNR), segmental SNR (SSNR), scale-invariant SNR (SI-SNR), and optimal SI-SNR (OSI-SNR). Further, many evaluation metrics have been proposed for measuring speech quality and intelligibility.