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Objective. Identifying the inter-crystal scatter (ICS) events and recovering the first interaction position enables the accurate determination of the line-of-response in positron emission tomography (PET). However, conventional silicon photomultiplier (SiPM) signal multiplexing methods based on two-dimensional (2D) charge-division circuits do not allow the detection of multiple gamma-ray interaction positions in a scintillation array coupled with a SiPM array. In this study, we propose a novel multiplexing method that can restore all the individual channel data from a smaller number of multiplexed channels using high-pass filters and neural networks. Approach.The number of output channels is reduced by summing the SiPM signals that have passed through high-pass filters with different time constants. Then, the signal amplitude of each SiPM channel is restored from the combined signal using an artificial neural network. This study explains the principle of this method in detail and demonstrates the results using 4:1 multiplexing as an example. The usefulness of this method was also demonstrated by its application in the identification of ICS events in 1-to-1 coupled LSO-SiPM PET detectors. Main results. The artificial neural network enabled accurate energy estimation for each SiPM channel. One of the high-pass filter sets with the lowest Cramér–Rao lower bound provided the best results, yielding R2 value of 0.99 between the true and estimated signals. The energy and flood histograms generated using the best-estimated signals were in good agreement with the ground truth. Additionally, the proposed method accurately estimated 2D energy deposit distribution in the LSO crystal array, allowing ICS event identification. Significance. The proposed method is potentially useful for ICS event recovery with a reduced number of array signal readout channels from a SiPM array.

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: SCC (Smart Cruise Control) and AEBS (Autonomous Emergency Braking System) are using various types of sensors data, so it is important to consider about sensor data reliability. In this paper, data from radar and vision sensor is fused by applying a Bayesian sensor fusion technique to improve the reliability of sensors data. Then, it presents a sensor fusion verification tool developed to monitor acquired sensors data and to verify sensor fusion results, efficiently. A parallel computing method was applied to reduce verification time and a series of simulation results of this method are discussed in detail.