<p>To correctly reproduce the appearance of different objects in computer graphics applications, numerous lighting models have been proposed over the past several decades. These models are among the most important components in the modern graphics pipeline since they decide the final pixel color shown in the generated images. More physically valid parameters and functions have been introduced into recent models. These parameters expanded the range of materials that can be represented and made virtual scenes more realistic, but they also made the lighting models more complex and dependent on measured data.</p> <p>Artificial neural networks, or neural networks are famous for their ability to deal with complex data and to approximate arbitrary functions. They have been adopted by many data-driven approaches for computer graphics and proven to be effective. Furthermore, neural networks have also been used by the artists for creative works and proven to have the ability of supporting creation of visual effects, animation and computational arts. Therefore, it is reasonable to consider artificial neural networks as potential tools for representing lighting models. Since shaders are used for general-purpose computing, neural networks can be further combined with modern graphics pipeline using shader implementation. </p> <p>In this research, the possibilities of shader-based neural networks to be used as an alternative to traditional lighting models are explored. Fully connected neural networks are implemented in fragment shader to reproduce lighting results in the graphics pipeline, and trained in compute shaders. Implemented networks are proved to be able to approximate mathematical lighting models. In this thesis, experiments are described to prove the ability of shader-based neural networks, to explore the proper network architecture and settings for different lighting models. Further explorations of possibilities of manually editing parameters are also described. Mean-square errors and runtime are taken as measurements of success to evaluate the experiments. Rendered images are also reported for visual comparison and evaluation.</p>

Passive Intermodulation is a type of distortion affecting the sensitive receiving signals in a cellular network, which is a growing problem in the telecommunication field. One way to mitigate this problem is through Passive Intermodulation Cancellation, where the predicted noise in a signal is modeled with polynomials. Recent experiments using neural networks instead of polynomials to model this noise have shown promising results. However, one drawback with neural networks is their lack of explainability. In this work, we identify a suitable method that provides explanations for this use case. We apply this technique to explain the neural networks used for Passive Intermodulation Cancellation and discuss the result with domain expertise. We show that the input space as well as the architecture could be altered, and propose an alternative architecture for the neural network used for Passive Intermodulation Cancellation. This alternative architecture leads to a significant reduction in trainable parameters, a finding which is valuable in a cellular network where resources are heavily constrained. When performing an explainability analysis of the alternative model, the explanations are also more in line with domain expertise. / Passiv Intermodulation är en typ av störning som påverkar de känsliga mottagarsignalerna i ett mobilnät. Detta är ett växande problem inom telekommunikation. Ett tillvägagångssätt för att motverka detta problem är genom passiv intermodulations-annullering, där störningarna modelleras med hjälp av polynomiska funktioner. Nyligen har experiment där neurala nätverk används istället för polynomiska funktioner för att modellera dessa störningar påvisat intressanta resultat. Användandet av neurala nätverk är dock förenat med vissa nackdelar, varav en är svårigheten att tyda och tolka utfall av neurala nätverk. I detta projekt identifieras en passande metod för att erbjuda förklaringar av neurala nätverk tränade för passiv intermodulations-annullering. Vi applicerar denna metod på nämnda neurala nätverk och utvärderar resultatet tillsammans med domänexpertis. Vi visar att formatet på indatan till neurala nätverket kan manipuleras, samt föreslår en alternativ arkitektur för neurala nätverk tränade för passiv intermodulations-annullering. Denna alternativa arkitektur innebär en avsevärd reduktion av antalet träningsbara parametrar, vilket är ett värdefullt resultat i samband med mobilnät där det finns kraftiga begränsningar på hårdvaruresurser. När vi applicerar metoder för att förklara utfall av denna alternativa arkitektur finner vi även att förklaringarna bättre motsvarar förväntningarna från domänexpertis.