A comprehensive survey on low-cost ECG acquisition systems: Advances on design specifications, challenges and future direction

A modular Vedic multiplier architecture for model-based design and deployment on FPGA platforms

Partial product addition in Vedic design-ripple carry adder design fir filter architecture for electro cardiogram (ECG) signal de-noising application

Warm Mix Asphalts (WMAs) are asphalt concretes produced at lower temperatures than traditional Hot Mix Asphalts (HMAs). Nonetheless, the above is not enough to diminish the environmental impacts associated with the road infrastructure industry. Accordingly, incorporating Recycled Concrete Aggregate (RCA) as a partial replacement for Natural Aggregates (NAs) in WMA design has been gaining notoriety in the literature as a viable alternative to increase sustainability. However, the eco-friendly manufacturing of WMA with RCA contents (WMA-RCA) is not easy to obtain satisfactorily because the RCA causes alterations in the mix design. Thus, this research proposes three (3) methods to determine the optimal design conditions (coarse RCA content) that minimize the environmental burdens caused by WMA-RCA production. The first method is a mathematical model based on Multiple Linear Regression (MLR), which is used as a benchmark for the other two methods. The second and third methods are computational models based on Artificial Intelligence (AI), i.e., Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs), respectively. Notably, the Life Cycle Assessment (LCA) was employed as the theoretical framework to support all the proposed models. Consequently, this study concludes that: (i) all the proposed methodological alternatives achieve results with a great accuracy; (ii) the GAs model is the most precise method in terms of error minimization; (iii) the MLR model is the fastest method in terms of execution time; and (iv) the ANNs model is the method that requires the longest time of running, and its exactness is at a midpoint concerning the other models. 

Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques

A new approach to adaptive threshold based method for QRS detection with fuzzy clustering

Low Power and Low Area VLSI implementation of Vedic design FIR filter for ECG signal de-noising

This paper presents a low complexity two dimensional (2D) circular symmetric Finite Impulse Response (FIR) filter design and implementation of architecture. The optimized circular symmetric 2D FIR filter is designed using a modified Park–McClellan transformation method and filter coefficients are quantized using a canonical signed digit (CSD) binary number format. The CSD encoded coefficients are optimized to reduce the number of adder/subtractors using common subexpression elimination (CSE) algorithms. Based on the modified filter coefficients, the two structures fully direct form and hybrid-II form 2D FIR filter architectures are implemented using CSD and CSD with Horizontal CSE and Vertical CSE techniques. The proposed architectures compared with the conventional symmetry 2D filters and state-of-the-art architectures in terms of area, power, and speed.

Design and implementation of low complexity circularly symmetric 2D FIR filter architectures

Early diagnosis of a brain tumour may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. In this study, first noise removed by median filter and dimensionality of datasets reduced by using random projection transformation (RPT). Next, the pre-processed images are clustered by using K-means and fuzzy c-means (FCM). In the very next step, the clustered images multi-features are fused by different data fusion approaches, and then segment the exact tumour area by using the active contour models such as level set method (LSM) and Chan-Vese (C-V). The performance of clustered based segmentation and fusion-based segmentation in terms of various fusion metrics. The results of both clustered based and fusion-based methods revealed that the CNN fusion-based segmentation performs better than clustered- based segmentation to detect the tumour with low segmentation error and minimal loss of information.

Performance analysis of CNN fusion based brain tumour detection using Chan-Vese and level set segmentation algorithms

Network densification becomes inevitable due to rapidly increasing mobile data traffic in the evolution of future cellular networks. Therefore, Heterogeneous Wireless Networks eliminate the challenges that are not covered in 4G-Long Term Evolution. The extensive contribution of the proposed work is to develop a cluster based algorithm that establishes the maximum node connections in the heterogeneous wireless network and also to implement effective Multi-User Generalized Frequency Division Multiplexing in the physical layer for future wireless communications. The presence of unconnected nodes (Base Stations) in the wireless network reduces the network efficiency and coverage capacity. Whenever a wireless device is connected to at least one isolated neighbor node (BS), it acts as a Virtual Clustered Head based on shortest path node and residual energy of the node. A Virtual Cluster (VC) based multipath routing is implemented for Heterogeneous Networks to maximize the Energy Efficiency (EE), Spectral Efficiency (SE), Channel Capacity (CC), and Network Capacity (NC) and minimize the Bit Error Rate and latency under different constraints in the network. The simulation results with GFDM modulation shows the improvement in the network performance and compared with existing OFDM results.

Performance Analysis of GFDM Modulation in Heterogeneous Network for 5G NR

Accurate QRS detection is an important first step for almost every electrocardiogram (ECG) signal analysis. However, detecting QRS is difficult, not only because of the large variety, but also as a result of interference caused by various types of noise. This paper employs a hybrid feature extraction technique of ECG signal for the detection of cardiac abnormalities. Noise removal and accurate QRS detection play a major role in the analysis of ECG signals. In this paper various types of noises such as additive white Gaussian noise, baseline wander and power line interference is eliminated to enhance the signal quality. This study proposes an improved QRS complex detection algorithm based on the combination of empirical mode decomposition-discrete wavelet transform (EMD-DWT) with threshold and compared with ordinary discrete wavelet transform. The system efficacy and performance have been evaluated using accuracy, sensitivity (Se), positive predictive value (PPV) and detection error rate (DER). The results show the high accuracy of the proposed EMD-DWT algorithm, which attains a detection error rate of 1.1233%, a sensitivity of 99.28%, and a positive predictive value of 99.99%, evaluated using the MIT-BIH arrhythmia database. The proposed algorithm improves the accuracy of QRS detection compared to state-of-art methods.

K. Satya Prasad

Papers

Low Power and Low Area VLSI implementation of Vedic design FIR filter for ECG signal de-noising

Design and implementation of low complexity circularly symmetric 2D FIR filter architectures

Performance analysis of CNN fusion based brain tumour detection using Chan-Vese and level set segmentation algorithms

Performance Analysis of GFDM Modulation in Heterogeneous Network for 5G NR

A hybrid EMD-DWT based algorithm for detection of QRS complex in electrocardiogram signal