Search results

In this research work, brief quantum-dot cellular automata (QCA) concepts are discussed through arithmetic and logic units. This work is most useful for nanoelectronic applications, VLSI industry mainly depends on this type of fault-tolerant QCA based arithmetic logic unit (ALU) design. The ALU design is mainly depending on set instructions and rules; these are maintained through low-power ultra-functional tricks only possible with QCA-based reversible arithmetic and logic unit for nanoelectronics. The main objective of this investigation is to design an ultra-low power and ultra-high-speed ALU design with QCA technology. The following QCA method has been implemented through reversible logic.

QCA logic is the main and critical condition for realizing NANO-scale design that delivers considerably fast integrate module, effective performable computation and is less energy efficiency at the nano-scale (QCA). Processors need an ALU in order to process and calculate data. Fault-resistant ALU in QCA technology utilizing reverse logic is the primary objective of this study. There are now two sections, i.e. reversible ALU (RAU), logical (LAU) and arithmetical (RAU).

A reversible 2 × 1 multiplexer based on the Fredkin gate (FRG) was developed to allow users to choose between arithmetic and logical operations. QCA full adders are also implemented to improve arithmetic operations' performance. The ALU is built using reversible logic gates that are fault-tolerant.

In contrast to earlier research, the suggested reversible multilayered ALU with reversible QCA operation is imported. The 8- and 16-bit ALU, as well as logical unit functioning, is designed through fewer gates, constant inputs and outputs. This implementation is designed on the Mentor Graphics QCA tool and verifies all functionalities.

The purpose of chopper amplifier is to provide the wideband frequency to support biomedical signals.

This paper proposes a chopper-stabilized amplifier with a cascoded operational transconductance amplifier. The high impedance loop is established using an MOS pseudo resistor and with a tunable MOS capacitor.

The total power consumption is 451 nW with a supplied voltage of 800 mV. The Gain and common mode rejection ratio are 48 dB and 78 dB, respectively.

All kinds of real time data analysis was not carried out, only few test samples related to EEG signals are validated because the real time chip was not manufactured due to funding issues.

The proposed work was validated with Monte-Carlo simulations. There is no external funding for the proposed work. So there is no fabrication for the design. But post simulations are performed.

The high impedance loop is established using an MOS pseudo resistor and with a tunable MOS capacitor. To the best of the author’s knowledge, this concept is completely novel and there are no publications on this work. All the modules designed for chopper amplifier are new concepts.

The purpose of this paper is to provide a shorter time cost, high-accuracy fault diagnosis method for water pumps. Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention. Considering the time-consuming empirical mode decomposition (EMD) method and the more efficient classification provided by the convolutional neural network (CNN) method, a novel classification method based on incomplete empirical mode decomposition (IEMD) and dual-input dual-channel convolutional neural network (DDCNN) composite data is proposed and applied to the fault diagnosis of water pumps.

This paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient (MFCC) and a neural network model of DDCNN. First, the sound signal is decomposed by IEMD to get numerous intrinsic mode functions (IMFs) and a residual (RES). Several IMFs and one RES are then extracted by MFCC features. Ultimately, the obtained features are split into two channels (IMFs one channel; RES one channel) and input into DDCNN.

The Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII dataset) is used to verify the practicability of the method. Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis. Compared with EMD, 51.52% of data preprocessing time, 67.25% of network training time and 63.7% of test time are saved and also improve accuracy.

This method can achieve higher accuracy in fault diagnosis with a shorter time cost. Therefore, the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.

This method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.