A Novel Scheme for Fault Tolerant Computing

A novel scheme for ensuring reliability in the operation of a combinational digital network has been offered in this paper. This has been achieved by making use of three copies of the same digital network along with two additional sub-networks, one of which consists of three additional control inputs, which can also be used as additional observable outputs. If both the said two sub-networks are fault free, then the primary output of the network in the present scheme will always give fault-free responses even if a fault (single or multiple) occurs in one of the three copies of the digital network under consideration. Unlike the Triple Modular Redundancy (TMR) scheme, the present scheme does not require any majority voter circuit. Furthermore, unlike the TMR scheme, the additional sub-networks in the present scheme can be tested off-line by predefined test input patterns.

Design of a Microwave Lowpass – Bandpass Filter using Deep Learning and Artificial Intelligence

In this paper, a lowpass – bandpass dual band microwave filter is designed by using deep learning and artificial intelligence. The designed filter has compact size and desirable pass bands. In the proposed filter, the resonators with Z-shaped and T-shaped lines are used to design the low pass channel, while coupling lines, stepped impedance resonators and open ended stubs are utilized to design the bandpass channel. Artificial neural network (ANN) and deep learning (DL) technique has been utilized to extract the proposed filter transfer function, so the values of the transmission zeros can be located in the desired frequency. This technique can also be used for the other electrical devices. The lowpass channel cut off frequency is 1 GHz, with better than 0.2 dB insertion loss. Also, the bandpass channel main frequency is designed at 2.4 GHz with 0.5 dB insertion loss in the passband.