Design of Digital IIR Filter with Conflicting Objectives Using Hybrid Gravitational Search Algorithm

Sidhu, Darshan Singh; Dhillon, J.S.; Kaur, Devinder

doi:10.1155/2015/282809

Cited by 13 publications

(3 citation statements)

References 18 publications

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“…Because of the successful inspection of the trembles and brained features of the device, various features obtained from the study are the electric signal interpreted by one or more classifcatory, which guarantees 89 percent of accuracy in comparison to the usual radial network. However, because the system consists of more overhead calculation than the usual radial network, reference [15] describes a method for analysing Parkinson's disease based on brain information that is based on genetic algorithms and intensive learning. Te computer collects UCI brain data, which is then processed by the kernel information in your wavelet's wavelet kernel information.…”

Section: Related Workmentioning

confidence: 99%

A Complex Brain Learning Skeleton Comprising Enriched Pattern Neural Network System for Next Era Internet of Things

Venkatesh¹,

Shukla

Ahanger

et al. 2023

Journal of Healthcare Engineering

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The Internet of Things (IoT) is now growing dramatically on various levels and helps to digitize various vital industries quickly. The most difficult obstacle for BCIs to overcome is the fact that not everyone has the same brain. Every new session requires the BCI to learn from the user’s brain, which is accomplished via the use of machine learning. However, this learning process is time-consuming. Calibration time refers to the amount of time it takes for the BCI to adapt to the user’s brain in order to properly categorize their thoughts and determine their meaning. The patient has had to wait an arduous and tiresome length of time for the system to be completely functioning up until now because of this calibration, which may take up to 20–30 minutes. The aim of this paper was to find a way to decrease the amount of time required for calibration to the smallest amount feasible. In the first section of this paper, a first effort is made to determine the optimum number of features required for the BCI to operate reasonably, taking into consideration all of the calibration data provided. When the results were averaged across five participants, the percentage of properly identified thoughts was just 67.15 percent. Transfer learning was used in order to improve the performance of the BCI while simultaneously decreasing the calibration time. It is feasible to decrease the amount of calibration required for the categorization of thoughts coming from a new target subject by using knowledge collected from previously recorded subjects to the greatest extent possible in transfer learning. It was determined that existing methods were superior, and a new methodology was created that required just 24 seconds of calibration data while accurately identifying 86.8% of the thoughts. In order to alleviate mental stress and anger, the system suggested fits effectively with a deep learning network. This paper proposes a brain learning framework that uses a neural network model that is complex in nature and uses IoT for data collection from various wearable devices, and the same can be used for modelling the brain functions. Aside from the fact that categorization performance is assessed, a more relevant metric is the number of letters per minute that a user transmits. In addition to evaluating classification performance, there are methods that evaluate the amount of time necessary to complete specified tasks.

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Section: Related Workmentioning

confidence: 99%

A Complex Brain Learning Skeleton Comprising Enriched Pattern Neural Network System for Next Era Internet of Things

Venkatesh¹,

Shukla

Ahanger

et al. 2023

Journal of Healthcare Engineering

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“…It justifies the hardware complexity. The modified 60-order filter as used has the reduced magnitude [ 30 ]. The overall comparison justified the effectiveness of the proposed filter response.…”

Section: Validation Of Existing Filter Designsmentioning

confidence: 99%

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Amhia

Wadhwani

2022

Journal of Healthcare Engineering

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Cardiovascular health and training success can be assessed using electrocardiogram (ECG) data. For over a quarter of a century, an individual’s resting heart rate is varying more. As a result, it has become the subject of inquiry and reveals the intricate relationship between the human body and its environment. The autonomic nervous system has impact on blood flow system based on the rate of heartbeats. However, heart rate variation (HRV) characteristics analysis throughout the time period has lack of physical activity information. In the presence of patient movement, ECG signal is suffering from hard artefacts. Time-varying HRV parameters can be derived from low-frequency (LF) and high-frequency (HF) domains of the correct frequency. However, sometimes it is critical to ensuring accurate detection of the R-peak position. The proposed ROIIR (reduced-order IIR) offers 8.8% improvement in peak-to-peak swing than earlier IIR filter. We present an advanced filtering algorithm that is used for R-peak detection.

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“…The design has achieved fast convergence and accurate IIR approximation. D. S. Sidhu et al [12] discussed IIR filter design using hybrid gravitational search algorithm (HGSA) to minimize magnitude response error, linear phase response error with matching the stability criterion. B. Singh et al [13] proposed digital IIR filter design using DE hybridized and pattern search technique.…”

mentioning

confidence: 99%

High performance IIR filter implementation on FPGA

Datta

Dutta

2021

Journal of Electrical Systems and Inf Technol

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This paper presents an improved design of reconfigurable infinite impulse response (IIR) filter that can be widely used in real-time applications. The proposed IIR design is realized by parallel–pipeline-based finite impulse response (FIR) filter. The FIR filters have excellent characteristics such as high stability, linear phase response and fewer finite precision errors. Hence, FIR-based IIR design is more attractive and selective in signal processing. In addition, the other two modern techniques such as look-ahead and two-level pipeline IIR filter designs are also discussed. All the said designs have been described in hardware description language and tested on Xilinx Virtex-5 field programmable gate array board. The implementation results show that the proposed FIR-based IIR design yields better performance in terms of hardware utilization, higher operating speed and lower power consumption compared to conventional IIR filter.

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Design of Digital IIR Filter with Conflicting Objectives Using Hybrid Gravitational Search Algorithm

Cited by 13 publications

References 18 publications

A Complex Brain Learning Skeleton Comprising Enriched Pattern Neural Network System for Next Era Internet of Things

A Complex Brain Learning Skeleton Comprising Enriched Pattern Neural Network System for Next Era Internet of Things

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

High performance IIR filter implementation on FPGA

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