With the increasing demand of autonomous machines, pixel-wise semantic segmentation for visual scene understanding needs to be not only accurate but also efficient for any potential real-time applications. In this paper, we propose CABiNet (Context Aggregated Bi-lateral Network), a dual branch convolutional neural network (CNN), with significantly lower computational costs as compared to the state-of-the-art, while maintaining a competitive prediction accuracy. Building upon the existing multi-branch architectures for high-speed semantic segmentation, we design a cheap high resolution branch for effective spatial detailing and a context branch with light-weight versions of global aggregation and local distribution blocks, potent to capture both long-range and local contextual dependencies required for accurate semantic segmentation, with low computational overheads. Specifically, we achieve 76.6% and 75.9% mIOU on Cityscapes validation and test sets respectively, at 76 FPS on an NVIDIA RTX 2080Ti and 8 FPS on a Jetson Xavier NX.
Epileptic is a neurological condition that affects approximately 50 million people worldwide. Epileptic seizure prediction lowers the risk of a patient’s life being endangered by a seizure that occurs unexpectedly. The latest seizure prediction methods are computationally intensive due to the complicated hand-crafted features they extract, and they take a lot of memory to store their parameters, which makes them Inappropriate for IoT and connected systems with limited capabilities. In this paper, a deep learning-based IoT framework for accurate epileptic seizure prediction is presented. The proposed method combines the feature extraction and classification stages into a single integrated system in which raw data heartbeat and temperature signals are implemented without any pre-processing, reducing computing complexity even further. A machine learning based prediction model is proposed that extracts the relevant information from the temperature, heartbeat and haemoglobin value using of machine learning algorithm The health condition of patient or person can be found and give some analysis result like normal or abnormal condition. If abnormal condition is observed then the system predicts some medicine or dosage based on health condition and also send alert message using of GSM. In this work, a location tracking of patient is also included and alert is sent to authorized person when the patient fall down or patient get panic or abnormal health
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