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In this paper, a new high-resolution digital imager based on a time multiplexing scheme is proposed. The imager produces a 256-grayscale image through capturing 256 successive frames that each belongs to a specific luminance range. Each pixel includes a 1-b analog-to-digital converter (ADC) and a single bit static memory to improve the fill factor. The in-pixel ADC is designed as a compact and fast converter to achieve a high-resolution and video-rate image sensor. The proposed sensor is designed and implemented in a standard 180-nm CMOS technology. The imager achieves an overall dynamic range of over 140 dB at video rate imaging. The pixel pitch is 18.3~\mu \textm and the fill factor is about 48%. The circuit operates at a supply voltage as low as 800 mV. At this supply voltage and at video rate imaging, its power consumption is about 4.33 nW. The proposed imager can directly perform some pre-processing algorithms, such as image segmentation and binarization. Additionally, the proposed method transfers the memory and process units of the pixels to the external of the sensor array so it provides a suitable structure for designing high-resolution, wide dynamic range, and fast general-purpose image sensors. [ABSTRACT FROM PUBLISHER]

One of the epileptic patients' challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients., (© 2024 IOP Publishing Ltd.)