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As the semiconductor and photonics industries grapple with mounting business pressures, weaving resourceefficiency into engineering education has evolved from a priority to an imperative. Under the umbrella of FUTUR-IC, this paper highlights novel pedagogical strategies at Bridgewater State University (BSU) aimed at equipping photonics and optical engineers to address today’s ecological challenges. We detail two complementary approaches that together form a cohesive educational framework. The first involves a newly introduced fresh year-level seminar on Resource Efficient Microchip Manufacturing, which immerses students in resource-efficiency metrics such as Life Cycle Intelligence and “design for resourceefficiency” principles. By interlinking photonic integration concepts with tangible business impact assessments, this course fosters an early appreciation of how advanced technologies can be developed responsibly, with reduced energy consumption and minimized waste. The second approach redefines senior-level engineering design courses to embed multifaceted resourceefficiency criteria in the design process. Through project-based learning and collaboration with industry partners, students integrate photonic solutions with data-driven metrics, refining their ability to propose holistic prototypes. These initiatives go beyond technical mastery to cultivate interdisciplinary collaboration and critical thinking. This work illustrates how an integrated approach to engineering education can spark the next generation of practitioners to design for both technological excellence and business viability. 

Many large multimedia applications require efficient processing of nearest neighbor queries. Often, multimedia data are represented as a collection of important high-dimensional feature vectors. Existing Locality Sensitive Hashing (LSH) techniques require users to find top-k similar feature vectors for each of the feature vectors that represent the query object. This leads to wasted and redundant work due to two main reasons: 1) not all feature vectors may contribute equally in finding the top-k similar multimedia objects, and 2) feature vectors are treated independently during query processing. Additionally, there is no theoretical guarantee on the returned multimedia results. In this work, we propose a practical and efficient indexing approach for finding top-k approximate nearest neighbors for multimedia data using LSH called mmLSH, which can provide theoretical guarantees on the returned multimedia results. Additionally, we present a buffer-conscious strategy to speed up the query processing. Experimental evaluation shows significant gains in performance time and accuracy for different real multimedia datasets when compared against state-of-the-art LSH techniques.