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This paper aims to present a single-block memory-based FFT processor design with a conflict-free addressing scheme for field-programmable gate arrays FPGAs with dual-port block memories. This study aims for a single-block dual-port memory-based N-point radix-2 FFT design that uses memory locations and spending minimum clock cycle.

A new memory-based Fast Fourier Transform (FFT) design that uses a dual-port memory block is proposed. Dual-port memory allows the design to perform two memory reads and writes in a single clock cycle. This approach achieves low operational clock and smallest memory simultaneously, excluding some small overhead for exceptional address changes. The methodology is to read from while writing to a memory location, eliminating the need for excess memory and additional clock cycles.

With the minimum memory size and the simplest architecture, radix-2 FFT and single-memory block are used. The number of clock pulses spent for all FFT operations does not provide much advantage for low-point FFT operations but is important for high-point FFT operations. With the developed algorithm, N memory is used, and the number of clock pulses spent for all FFT stages is (N/2 +1)log2N for all FFT operations.

This is an original paper, which has simultaneously in whole or in part been submitted anywhere else.

Synthetic aperture radar (SAR) imaging is the most computational intensive algorithm and this makes its implementation challenging for real-time application. This paper aims to present the chirp-scaling algorithm (CSA) for real-time SAR applications, using advanced field programmable gate array (FPGA) processor.

A chirp signal is generated and compressed using range Doppler algorithm in MATAB for validation. Fast Fourier transform (FFT) and multiplication operations with complex data types are the major units requiring heavy computation. Therefore, hardware acceleration is proposed and implemented on NEON-FPGA processor using NE10 and CEPHES library.

The heuristic analysis of the algorithm using timing analysis and resource usage is presented. It has been observed that FFT execution time is reduced by 61% by boosting the performance of the algorithm and speed of multiplication operation has been doubled because of the optimization.

Very few literatures have presented the FPGA-based SAR imaging implementation, where analysis of windowing technique was a major interest. This is a unique approach to implement the SAR CSA using a hybrid approach of hardware–software integration on Zynq FPGA. The timing analysis propagates that it is suitable to use this model for real-time SAR applications.

In recent years, multiuser-multiple-input multiple-output (MU-MIMO)-based wireless communication system has emerged as a prominent 5G technique that has several advantages over conventional MIMO systems such as high data rate and channel capacity. In this paper, the authors introduce a novel low-complexity radix factorization-based fast Fourier transform (FFT) as a multibeamformer and maximal likelihood-MU detection (ML-MUD) techniques as an optimal signal subdetector which results with considerable complexity reduction with intolerable error rate performance.

The proposed radix-factorized FFT-multibeamforming (RF-FFT-MBF) architectures have the potential to reduce both hardware complexity and energy consumptions as compared to its state-of-the-art methods while meeting the throughput requirements of emerging 5G devices. Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors.

Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.

Here through simulation results, the efficiency of the scaled ML subdetector system is compared with the conventional ML detectors. Through experimental results, it is well proved that the proposed detector offers significant hardware and energy efficiency with the least possible error rate performance overhead.

COVID-19 would have a far-reaching impact on the international health-care industry and the patients. For COVID-19, there is a need for unique screening tests to reliably and rapidly determine who is infected. Medical COVID images protection is critical when data pertaining to computer images are being transmitted through public networks in health information systems.

Medical images such as computed tomography (CT) play key role in the diagnosis of COVID-19 patients. Neural networks-based methods are designed to detect COVID patients using chest CT scan images. And CT images are transmitted securely in health information systems.

The authors hereby examine neural networks-based COVID diagnosis methods using chest CT scan images and secure transmission of CT images for health information systems. For screening patients infected with COVID-19, a new approach using convolutional neural networks is proposed, and its output is simulated.

The required patient’s chest CT scan images have been taken from online databases such as GitHub. The experiments show that neural networks-based methods are effective in the diagnosis of COVID-19 patients using chest CT scan images.

The purpose of this paper is to inspect strategic placing of different signal processing techniques like wavelet transform (WT), discrete Hilbert transform (DHT) and fast Fourier transform (FFT) to acquire the qualitative detection of rotor fault in a variable frequency drive-fed induction motor under challenging low slip conditions.

The algorithm is developed using Q2.14 bit format of Xilinx System Generator (XSG)-DSP design tool in MATLAB. The developed algorithm in XSG-MATLAB can be implemented easily in field programmable gate array, as a provision to generate the necessary VHDL code is available by its graphical user interface.

The applicability of WT is ensured by the effective procedure of base wavelet selection, which is the novelty of the work. It is found that low-order Daubechies (db) wavelets show decent shape matching with current envelope rather than raw current signal. This fact allows to use db1-based discrete wavelet transform-inverse discrete wavelet transform, where economic and multiplier-less design is possible. Prominent identity of 2sf_s component is found even at low FFT points due to the application of suitable base wavelet.

The proposed method is found to be effective and hardware-friendly, which can be used to design a low-cost diagnostic instrument for industrial applications.

Rapid advancements in computer technologies over the past decade have recorded significant growth in the area of computational micromagnetics. As a result, current micromagnetic codes exploit the scalability offered by parallel and distributed computer architectures to deliver maximum performance. The purpose of this paper is to present a review, which explores various aspects of this relationship.

The authors arrange the theme for this paper around the micromagnetic code development process. The review involves a discussion of the micromagnetic model, some new parallel architectures, and computational aspects based on different numerical methods.

As current micromagnetic code is not readily portable to different architectures, most of the development effort goes towards this area, with a focus on writing/rewriting code for streaming hardware (particularly graphic cards).

The paper identifies key challenges and avenues for further research on computational aspects of micromagnetic modelling and suggests recommendations based on the review.

The suggested work examines the latest developments such as the techniques employed for allocation of power, browser techniques, modern analysis and bandwidth efficiency of nonorthogonal multiple accesses (NOMA) in the network of 5G. Furthermore, the proposed work also illustrates the performance of NOMA when it is combined with various techniques of wireless communication namely network coding, multiple-input multiple-output (MIMO), space-time coding, collective communications, as well as many more. In the case of the MIMO system, the proposed research work specifically deals with a less complex recursive linear minimum mean square error (LMMSE) multiuser detector along with NOMA (MIMO-NOMA); here the multiple-antenna base station (BS) and multiple single-antenna users interact with each other instantaneously. Although LMMSE is a linear detector with a low intricacy, it performs poorly in multiuser identification because of the incompatibility between LMMSE identification and multiuser decoding. Thus, to obtain a desirable iterative identification rate, the proposed research work presents matching constraints among the decoders and identifiers of MIMO-NOMA.

To improve the performance in 5G technologies as well as in cellular communication, the NOMA technique is employed and contemplated as one of the best methodologies for accessing radio. The above-stated technique offers several advantages such as enhanced spectrum performance in contrast to the high-capacity orthogonal multiple access (OMA) approach that is also known as orthogonal frequency division multiple access (OFDMA). Code and power domain are some of the categories of the NOMA technique. The suggested research work mainly concentrates on the technique of NOMA, which is based on the power domain. This approach correspondingly makes use of superposition coding (SC) as well as successive interference cancellation (SIC) at source and recipient. For the fifth-generation applications, the network-level, as well as user-experienced data rate prerequisites, are successfully illustrated by various researchers.

The suggested combined methodology such as MIMO-NOMA demonstrates a synchronized iterative LMMSE system that can accomplish the optimized efficiency of symmetric MIMO NOMA with several users. To transmit the information from sender to the receiver, hybrid methodologies are confined to 2 × 2 as well as 4 × 4 antenna arrays, and thereby parameters such as PAPR, BER, SNR are analyzed and efficiency for various modulation strategies such as BPSK and QAM_j (j should vary from 8,16,32,64) are computed.

The proposed hybrid MIMO-NOMA methodologies are synchronized in terms of iterative process for optimization of LMMSE that can accomplish the optimized efficiency of symmetric for several users under different noisy conditions. From the obtained simulated results, it is found, there are 18%, 23% 16%, and 8% improvement in terms of Bit Error Rate (BER), Least Minimum Mean Squared Error (LMMSE), Peak to Average Power Ratio (PAPR), and capacity of channel respectively for Binary Phase Shift Key (BPSK) and Quadrature Amplitude Modulation (QAM) modulation techniques.

The purpose of this study aims to focus on the detection and identification of the broken rotor bars (BRBs) of a squirrel cage induction motor (SCIM). The presented diagnosis technique is based on artificial neural networks (NNs) that use as inputs the results of the spectral analysis using the fast Fourier transform (FFT) of the reduced Park’s vector modulus (RPVM), along with the load values in which the motor operates.

First, this paper presents a comparative study between FFT applied on Hilbert modulus, Park’s vector modulus and RPVM to extract feature frequencies of BRB faults. Moreover, the extracted features of FFT applied to RPVM and the load values were selected as NNs’ inputs for the detection of the number of BRBs.

The obtained simulation results using MATLAB (Matrix Laboratory) environment show the effectiveness and accuracy of the proposed NNs based approach.

The current paper presents a novel diagnostic method for BRBs’ fault detection in SCIM, based on the combination between the signal processing analysis (FFT of RPVM) and artificial intelligence (NNs).