With the continued innovation of deep neural networks, spiking neural networks (SNNs) that more closely resemble biological brain synapses have attracted attention because of their low power consumption. Unlike artificial neural networks (ANNs), for continuous data values, they must employ an encoding process to convert the values to spike trains, suppressing the SNN's performance. To avoid this degradation, the incoming analog signal must be regulated prior to the encoding process, which is also realized in living things eg, the basement membranes of humans mechanically perform the Fourier transform. To this end, we combine an ANN and an SNN to build ANN-to-SNN hybrid neural networks (HNNs) that improve the concerned performance. To qualify this performance and robustness, MNIST and CIFAR-10 image datasets are used for various classification tasks in which the training and encoding methods changes. In addition, we present simultaneous and separate training methods for the artificial and spiking layers, considering the encoding methods of each. We find that increasing the number of artificial layers at the expense of spiking layers improves the HNN performance. For straightforward datasets such as MNIST, similar performances as ANN's are achieved by using duplicate coding and separate learning. However, for more complex tasks, the use of Gaussian coding and simultaneous learning is found to improve the accuracy of the HNN while lower power consumption.

Real-time database systems have the properties of database and real-time systems. This means they must keep timing constraints of transactions as required in real-time systems, and at the same time ensure database consistency as required in database systems. Real-time concurrency control is a general approach for resolving this conflict. In this type of control, a concurrency control technique for database systems is integrated with a task scheduling technique for real-time systems. This paper surveys previous studies on real-time concurrency control and considers future research directions.

This paper describes the architecture and storage structure of a new interactive multimedia information system called VideoReality. VideoReality is based on a visual conducting model, which describes the information retrieval process that occurs when people observe visible objects in the real world. VideoReality provides a spatial and temporal spread of a virtual video space from a set of stored video streams. The video space has a three-layered structure similar to that of the ANSI/X3/SPARC three-schema architecture. Users can move their eyes and watch objects freely in the video space, just as if they were viewing the real world. This paper also presents a prototype application system called Electronic Aquarium'