Thin-film solar cells are predominately designed similar to a stacked structure. Optimizing the layer thicknesses in this stack structure is crucial to extract the best efficiency of the solar cell. The commonplace method used in optimization simulations, such as for optimizing the optical spacer layers’ thicknesses, is the parameter sweep. Our simulation study shows that the implementation of a meta-heuristic method like the genetic algorithm results in a significantly faster and accurate search method when compared to the brute-force parameter sweep method in both single and multi-layer optimization. While other sweep methods can also outperform the brute-force method, they do not consistently exhibit 100% accuracy in the optimized results like our genetic algorithm. We have used a well-studied P3HT-based structure to test our algorithm. Our best-case scenario was observed to use 60.84% fewer simulations than the brute-force method.

Images can convey emotion just like music. If that's so, then it might be possible that, given an image, one can obtain a music that can produce a similar reaction from the listener/viewer. The challenge lies in how to do that. In this paper, we analyze the image using the HSV color space model and assume that each one of the three components have a relation with basic music elements, like tone, pitch, rhythm and loudness. The image is then scanned from left to right and top to bottom in order to generate a sequence of notes. In the end, the emotional Mean Opinion Score (MOS) is used to evaluate the performance of the proposed method. This work could prove to be a very important contribution to the field of HCI because it can improve the interaction between computers and humans who are visually and/or hearing impaired. In the current work, we only consider two emotions; positive and negative.

Generating music with emotion similar to that of an input video is a very relevant issue nowadays. Video content creators and automatic movie directors benefit from maintaining their viewers engaged, which can be facilitated by producing novel material eliciting stronger emotions in them. Moreover, there is currently a demand for more empathetic computers to aid humans in applications such as augmenting the perception ability of visually- and/or hearing-impaired people. Current approaches overlook the video’s emotional characteristics in the music generation step, only consider static images instead of videos, are unable to generate novel music, and require a high level of human effort and skills. In this study, we propose a novel hybrid deep neural network that uses an Adaptive Neuro-Fuzzy Inference System to predict a video’s emotion from its visual features and a deep Long Short-Term Memory Recurrent Neural Network to generate its corresponding audio signals with similar emotional inkling. The former is able to appropriately model emotions due to its fuzzy properties, and the latter is able to model data with dynamic time properties well due to the availability of the previous hidden state information. The novelty of our proposed method lies in the extraction of visual emotional features in order to transform them into audio signals with corresponding emotional aspects for users. Quantitative experiments show low mean absolute errors of 0.217 and 0.255 in the Lindsey and DEAP datasets, respectively, and similar global features in the spectrograms. This indicates that our model is able to appropriately perform domain transformation between visual and audio features. Based on experimental results, our model can effectively generate an audio that matches the scene eliciting a similar emotion from the viewer in both datasets, and music generated by our model is also chosen more often (code available online at https://github.com/gcunhase/Emotional-Video-to-Audio-with-ANFIS-DeepRNN).

This work tackles sentence correction in the lan-guage domain by approaching it as a sequence to sequence (seq2seq) problem with the help of temporal hierarchies. It does so by implementing a Multiple Timescales model of the Gated Recurrent Unit (MTGRU) in a Recurrent Neural Network (RNN) Encoder-Decoder framework, which can perform more meaningful data abstraction even in the presence of errors. The proposed language correction model is compared to three baseline models: conventional RNN, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU); by using a newly built dataset that consists of incorrect and correct sentences as input and target respectively. The result shows that the MTGRU model has a better generalization performance and outperforms all three models on the BLEU-n evaluation metric.