Abstract. The process of medical device innovation involves an iterative method that focuses on designing innovative, device-oriented solutions that address unmet clinical needs. This process has been applied to the field of biophotonics with many notable successes. Device innovation begins with identifying an unmet clinical need and evaluating this need through a variety of lenses, including currently existing solutions for the need, stakeholders who are interested in the need, and the market that will support an innovative solution. Only once the clinical need is understood in detail can the invention process begin. The ideation phase often involves multiple levels of brainstorming and prototyping with the aim of addressing technical and clinical questions early and in a cost-efficient manner. Once potential solutions are found, they are tested against a number of known translational factors, including intellectual property, regulatory, and reimbursement landscapes. Only when the solution matches the clinical need, the next phase of building a "to market" strategy should begin. Most aspects of the innovation process can be conducted relatively quickly and without significant capital expense. This white paper focuses on key points of the medical device innovation method and how the field of biophotonics has been applied within this framework to generate clinical and commercial success.

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There exists many resource allocation problems in the field of wireless communications which can be formulated as the generalized assignment problems (GAP). GAP is a generic form of linear sum assignment problem (LSAP) and is more challenging to solve owing to the presence of both equality and inequality constraints. We propose a novel deep unsupervised learning (DUL) approach to solve GAP in a time-efficient manner. More specifically, we propose a new approach that facilitates to train a deep neural network (DNN) using a customized loss function. This customized loss function constitutes the objective function and penalty terms corresponding to both equality and inequality constraints. Furthermore, we propose to employ a Softmax activation function at the output of DNN along with tensor splitting which simplifies the customized loss function and guarantees to meet the equality constraint. As a case-study, we consider a typical user-association problem in a wireless network, formulate it as GAP, and consequently solve it using our proposed DUL approach. Numerical results demonstrate that the proposed DUL approach provides near-optimal results with significantly lower time-complexity.Index Terms-Deep neural networks (DNNs), generalized assignment problem (GAP), unsupervised learning (UL), userassociation and wireless networks.