Drowsy driving is one of the major problems which has led to many road accidents. Electroencephalography (EEG) is one of the most reliable sources to detect sleep on-set while driving as there is the direct involvement of biological signals. The present work focuses on detecting driver’s alertness using the deep neural network architecture, which is built using ResNets and encoder-decoder based sequence to sequence models with attention decoder. The ResNets with the skip connections allow training the network deeper with a reduced loss function and training error. The model is built to reduce the complex computations required for feature extraction. The ResNets also help in retaining the features from the previous layer and do not require different filters for frequency and time-invariant features. The output of ResNets, the features are input to encoder-decoder based sequence to sequence models, built using Bi-directional long-short memories. Sequence to Sequence model learns the complex features of the signal and analyze the output of past and future states simultaneously for classification of drowsy/sleepstage-1 and alert stages. Also, to overcome the unequal distribution (class-imbalance) data problem present in the datasets, the proposed loss functions help in achieving the identical error for both majority and minority classes during the raining of the network for each sleep stage. The model provides an overall-accuracy of 87.92% and 87.05%, a macro-F1-core of 78.06%, and 79.66% and Cohen's-kappa score of 0.78 and 0.79 for the Sleep-EDF 2013 and 2018 data sets respectively.
Automotive electronics is a course that requires skills from multiple disciplines including, but not limited to, mechanical, control, computer science, and electronics. The course is introduced to address the needs of embedded and automotive industries, hence providing the necessary knowledge and skills required for those industries. The objective of the curriculum is to enhance learning and improve student's implementation skills. In this paper, we propose to introduce the exercises including real-world case studies and experiential learning. The major challenge of teaching this course was to teach mechanical concepts for electrical science students and to develop electronics for mechanical systems. The practical demo sessions by automobile labs gave the desired foundation for the course. The engine management concepts were taught using a very popular simulation software, AT Electronics tool, which is a combination of electronics and diagnostics. This activity gave a real feel of engine management systems to learn how complex systems work and to diagnose faults with them. The paper also discusses another major activity in the form of course projects. The course projects resulted in the application of domain knowledge and improvement of skills by using appropriate tools. In addition to these activities, all regular classes included animations and video presentations to make the concepts clearer. Special lectures by industry experts were also arranged to give the students a wide perspective of the subject. The paper discusses the impact of these activities in the form of student feedback, placement results, and participation in technical events. This experiential learning helped the students to improve comprehensive application ability and innovative consciousness.
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