Equalization Filter Research Articles

RECEPTION ALGORITHM AS A WHOLE WITH ELEMENT-BY-ELEMENT DECISION MAKING USING THE PHASE MODULATION METHOD

The article is aimed to provide reception algorithm as a whole with element-by-element decision making. It belongs to the group of decision, making algorithms with feedback. It is used to demodulate discrete signals that have passed through a memory channel. The study presents an example of signal processing of a binary phase modulator. This example shows processing features that are not obvious for considering of the mathematical formula that describes demodulation algorithm. Another algorithm was developed for a quadrature phase modulator as well. The noise immunity for additive gaussian noise was estimated by the numerical simulations. The same researches was carried out using an equalizer filter that suppresses channel memory effect. Simulation results were used as the base for the graphs of the error probability versus signal-to-noise ratio.

Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm

Optimization of Parametric Equalizer Filters in In-Vehicle Audio Systems with a Genetic Algorithm

Multiplexing and demultiplexing acoustic orbital angular momentum beams in complex environments

The spiral sound waves carrying orbital angular momentum (OAM) are significantly more efficient in delivering information and energy than the ordinary plane waves. A major bottleneck in exploring the use of acoustic OAM is the excitation and detection of OAM in an arbitrary environment. Here we propose a scheme combining the multichannel least-mean-square (LMS) algorithm and an improved virtual rotating receiver (VRR) method to multiplex and demultiplex acoustic OAM beams in various complex environments. We experimentally demonstrate that the LMS-VRR scheme can excite and detect the OAM beams in indoor and outdoor real-world environments. We use the LMS algorithm to multiplex OAM beams with up to 40 channels at a circular microphone array by compensating the source signals with the equalization filters. On the other hand, the circular microphone array works as a VRR to demultiplex the OAM beams from the rotational Doppler effect. Additional preprocesses are taken to eliminate the plane wave pollution for the VRR method. The acoustic OAM beams have been multiplexed and demultiplexed over 20 m away from the sound sources on a shady lane. Our work has shown practical prospects in covert communication, and shall further unleash the application potential of acoustic OAM beams by removing the restrictions on exciting and detecting OAM in complex environments.

Low-complexity reconfigurable FIR lowpass equalizers for polynomial channel models

This paper introduces realizations of a reconfigurable finite-impulse-response (FIR) filter for simultaneous equalization and lowpass filtering. The main advantage of the proposed solutions is computational complexity reduction compared to existing solutions for a given performance, which leads to reduced hardware complexity. The proposed structures employ properties of both a variable bandwidth (VBW) filter and a variable equalizer (VE) with variable coefficients. The overall transfer function of the proposed reconfigurable lowpass equalizer (RLPE) is a weighted linear combination of fixed subfilters where the weights are directly determined by the bandwidth and one or several parameters of the channel needed to be equalized. The paper provides design procedures based on minimax optimization and introduces a fast design method for the filter with several variable parameters that can substantially decrease the design time. Filter order estimation expressions as well as complexity expressions are presented for all proposed realizations. Design examples include comparison of the RLPE structures and a common approach of using a regular FIR equalization filter requiring online redesign when the bandwidth or channel characteristics are changed. It is shown that the number of general multiplications can be reduced up to 91% using the proposed RLPE.

Ultrasonic Through-Metal Communication Based on Deep-Learning-Assisted Echo Cancellation.

Ultrasound is extremely efficient for wireless signal transmission through metal barriers due to no limit of the Faraday shielding effect. Echoing in the ultrasonic channel is one of the most challenging obstacles to performing high-quality communication, which is generally coped with by using a channel equalizer or pre-distorting filter. In this study, a deep learning algorithm called a dual-path recurrent neural network (DPRNN) was investigated for echo cancellation in an ultrasonic through-metal communication system. The actual system was constructed based on the combination of software and hardware, consisting of a pair of ultrasonic transducers, an FPGA module, some lab-made circuits, etc. The approach of DPRNN echo cancellation was applied to signals with a different signal-to-noise ratio (SNR) at a 2 Mbps transmission rate, achieving higher than 20 dB SNR improvement for all situations. Furthermore, this approach was successfully used for image transmission through a 50 mm thick aluminum plate, exhibiting a 24.8 dB peak-signal-to-noise ratio (PSNR) and a about 95% structural similarity index measure (SSIM). Additionally, compared with three other echo cancellation methods-LMS, RLS and PNLMS-DPRNN has demonstrated higher efficiency. All those results firmly validate that the DPRNN algorithm is a powerful tool to conduct echo cancellation and enhance the performance of ultrasonic through-metal transmission.

Automated weather forecasting and field monitoring using GRU-CNN model along with IoT to support precision agriculture

India's agricultural sector is dwindling, which has an impact on ecosystem output. Smart farming increases crop productivity by reducing waste and making effective use of fertiliser. Various Machine Learning techniques together with IoT enabled are developed for precision agriculture. However, the existing techniques face difficulties in forecasting weather and predicting disease accurately. In this work, a system is developed to automate weather forecasting and field monitoring using sensors. Wireless transmission is used to transfer sensor data to a web server database. In the first step, the collected data is pre-processed using normalization and mean based missing value imputation to convert the raw data into meaningful data. Pre-processed data is then fed into Gated Recurrent Unit (GRU) for forecasting the weather condition. In the second step, crop images acquired from sensors for disease prediction are pre-processed using Adaptive Gaussian Filter for noise removal and Dynamic Histogram Equalization for contrast enhancement of image. Pre-processed images are then fed into ResNet50 for feature extraction and classification. Using these predicted data, in case of any horrible weather conditions and soil conditions, remedial action will be automatically taken by the systems. On the other hand, regarding the information about horrible conditions and in presence of pests and diseases, an alert message will be sent to the farmers. The proposed method is tested with several metrics which attain better performance like 94% accuracy for weather forecasting and 98% accuracy for field monitoring. Thus the proposed IoT and deep learning based model can support farmers to achieve a high quantity of crop production in lesser time.

Coherent optical transmitter IQ imbalance mitigation with embedded carrier phase and frequency offset estimation.

We present a 4×4 real-valued channel equalizer with embedded phase estimator, designed for carrier phase and frequency offset estimation and compensation in coherent optical communications with in-phase/quadrature (IQ) impairments. These impairments include IQ timing skew, gain imbalance, and quadrature phase errors at the transmitter side. To achieve adaptive control of the equalizer's filter coefficients, we employ the decision-directed least mean square (DD-LMS) algorithm. This algorithm minimizes the error between the filter outputs and the desired signals in a symbol-by-symbol manner, resulting in faster channel coefficients adaptation speed. Simulation results for a 60 GBaud polarization-multiplexing 16 quadrature amplitude modulation (PM-16QAM) signal demonstrate that our proposed equalizer outperforms a 2×2 cascaded multi modulus algorithm-based (CMMA-based) equalizer, a 2×2 complex-valued method based on the DD-LMS algorithm, and the 4×4 real-valued equalizer without embedded frequency offset estimation (FOE), when the transmitter IQ impairments exist. Experimental validation is also conducted for the 60 GBaud PM-16QAM and 45 GBaud PM-64QAM signals. Similar to the simulation results, our experiments show that the proposed 4×4 real-valued equalizer with embedded FOE can achieve effective SNR penalties of less than 0.41 dB at 7 ps skew, 1.33 dB at 3.5 dB gain imbalance, and 1.64 dB at the bias voltage shift of 0.5 V for the 60 GBaud PM-16QAM signal. Finally, our experimental results confirm the effectiveness of our proposed method in carrier phase estimation as well as the frequency offset compensation, when compared with 4×4 real-valued equalizer without embedded FOE.

An efficient equalization filter design approach for the accurate sound reproduction in a room environment with wide-band content

In some sound reproduction applications, a desired sound field needs to be reproduced by remote loudspeakers in a room with sufficient accuracy over a wide frequency band. When low-frequency room modes exist and the low-frequency content is required to be reproduced accurately, an equalization filter with a long time-domain response may be necessary, resulting in a filter with a significantly high filter order, especially when the sampling rate is high for the high-frequency content. This imposes computational challenges on both filter design and implementation. In this article, a convex formulation is proposed for the wide-band sound reproduction filter design. To address issues due to the low-frequency room modes, a reduced-order technique is then proposed based on a sub-band filter structure where a low sampling rate is used to equalize the low-frequency room mode characteristics, and a high sampling rate is used for high-frequency features which usually has a short time span in response. The computational time can be reduced by several orders of magnitude and the numerical stability can be improved when the proposed order reduction method is used to eliminate redundant orders. Simulation and experimental results confirm the accuracy and efficiency advantages of the proposed method.

Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands

Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands

Neutrosophic Fuzzy Logic based SVM approach for Enhanced Skin Cancer Prediction

In this study, a thorough methodology is used to present a unique way for improving skin cancer prediction accuracy. The research uses sophisticated preprocessing methods, such as the Frost filter for noise reduction and histogram equalization for contrast enhancement, to boost contrast on dermoscopic pictures from various sources, using an ISIC 2020 dataset. These actions greatly raise the dermoscopic pictures’ overall quality and usefulness for diagnosis. Utilizing labeled data for training, we offer a Fuzzy-based C-means clustering technique based on Neutrosophic Logic during the segmentation phase. In order to overcome ambiguities in skin lesion segmentation, the neutrosophic set—a groundbreaking idea in philosophy—is used. The suggested model enhances the accuracy of segmentation by modifying the neutrosophic set functions. For precise prediction, the approach combines Support Vector Machine (SVM) classification with Histogram of Oriented Gradient (HOG) feature extraction. While SVM, a supervised learning algorithm, diagnoses skin lesions based on the collected features, HOG features capture gradient information. To improve object recognition and classification, the HOG-SVM architecture is made to methodically collect and quantify essential information using dermoscopic pictures. The use of Neutrosophic Fuzzy Logic, which combines the benefits of fuzzy clustering with neutrosophic sets to produce more precise and nuanced predictions, sets the suggested method apart. The integration of different approaches into a holistic solution for skin cancer prediction is what makes the proposed study innovative. Findings and performance analysis show of the HOG-SVM method exhibits an outstanding accuracy of 98.69%, outperforming LR, KNN, and GNB methods. Python software is used to accomplish the suggested approach. This discovery opens up a possible path for better skin cancer diagnosis and advances the rapidly developing fields of dermatology and medical image processing.

Face Recognition Based Attendance System Using Haar Cascade Algorithm with Histogram Equalization and Median Blur Filter

The attendance system is one of the mandatory activities in teaching and learning activities in an educational environment. Unfortunately, many still use a manual attendance system, which is inefficient, and fraud often occurs by manipulating attendance data. In this research, a face recognition-based attendance system was created. The face detection and recognition system use the haar cascade method with image preprocessing, namely histogram equalization and median blur filter. This system can provide output in the form of CSV documents containing attendance data that has been done in real time so that attendance data recording becomes more efficient than before. There is a significant difference in face recognition without and with image preprocessing. Without image preprocessing, the average face recognition accuracy rate is 71.2%. In face recognition with image preprocessing, the face recognition accuracy rate is 91.5%. Therefore, the use of image preprocessing can improve image quality and significantly increase the accuracy of face recognition. In addition, the CSV document is successfully generated automatically after the attendance process is carried out, which is equipped with user data, attendance date, and time when the user makes attendance to avoid fraud in the form of manipulation of attendance data by users.

Low complexity MIMO equalization for long-haul SDM systems

Space-division multiplexing (SDM) has been expected to support the continuous growth of transmission capacity. However, it suffers from high computation complexity that limits its physical implementations. In this paper, we propose and experimentally demonstrate a low-complexity MIMO equalization method to leverage the sparsity of weights and reduce the complexity by L1&L2-regularization in long-haul space-division multiplexing (SDM) systems. The L1-regularization finds the sparse solution of equalizer filters and substitutes it for optimal solution, reducing the complexity with performance degradation. On the other hand, the L2-regularization tends to produce a smoother estimation than L1 regularization and is therefore more robust to large variance. We conduct a 39.87-GBaud QPSK coherent optical transmission experiment based on a 4-core coupled-core fiber with the transmission distance from 1206-km to 7236-km. Comparisons on the equalization performance and computational complexity show that the sparse equalizer using L1&L2-regularization achieves a 30% reduction in complexity at the similar system performance, compared with the traditional time-domain MIMO.

Adaptive electro-optic equalization in low-power coherent-lite interconnects.

In this paper, we propose an adaptive electro-optic equalizer for short-reach interconnects employing coherent modulation and detection. The equalizer consists of two main components: an optical equalizer filter and an electronic controller. The equalizer filter compensates for the linear dispersion occurring in the channel, while the electronic controller adaptively determines the weight coefficients of the equalizer. To achieve adaptive adjustment, we have introduced a random search algorithm. Extensive simulations have been conducted to evaluate the performance of the equalizer in a 120Gbaud homodyne coherent link, and the results show great promise. The proposed equalizer has the potential to greatly improve the overall power efficiencies of receivers in short-reach coherent-lite interconnects.

An Early Detection and Classification of Alzheimer's Disease Framework Based on ResNet-50.

The objective of this study is to develop a more effective early detection system for Alzheimer's disease (AD) using a Deep Residual Network (ResNet) model by addressing the issue of convolutional layers in conventional Convolutional Neural Networks (CNN) and applying image preprocessing techniques. The proposed method involves using Contrast Limited Adaptive Histogram Equalizer (CLAHE) and Boosted Anisotropic Diffusion Filters (BADF) for equalization and noise removal and K-means clustering for segmentation. A ResNet-50 model with shortcut links between three residual layers is proposed to extract features more efficiently. ResNet-50 is preferred over other ResNet types due to its intermediate depth, striking a balance between computational efficiency and improved performance, making it a widely adopted and effective architecture for various computer vision tasks. While other ResNet variations may offer higher depths, they are more prone to overfitting and computational complexity, which can hinder their practical application. The proposed method is evaluated on a dataset of MRI scans of AD patients. The proposed method achieved high accuracy and minimum losses of 95% and 0.12, respectively. While some models showed better accuracy, they were prone to overfitting. In contrast, the suggested framework, based on the ResNet-50 model, demonstrated superior performance in terms of various performance metrics, providing a robust and reliable approach to Alzheimer's disease categorization. The proposed ResNet-50 model with shortcut links between three residual layers, combined with image preprocessing techniques, provides an effective early detection system for AD. The study demonstrates the potential of deep learning and image processing techniques in developing accurate and efficient diagnostic tools for AD. The proposed method improves the existing approaches to AD classification and provides a promising framework for future research in this area.

Correlating spirometry findings with auscultation sounds for diagnosis of respiratory diseases

Spirometry is the pulmonary function test (PFT) used for the diagnosis and severity measurement of respiratory illness. Although it is a non-invasive procedure still requires expertise, patient cooperation and repeated maneuver. Auscultation is the conventional method doctors use in the clinical environment. It can be used for early, efficient and remote diagnosis of respiratory diseases. In this paper, a method is proposed which can classify auscultation sounds collected from a simple stethoscope in OPD settings into three major categories, i.e., healthy, obstructive and restrictive which is traditionally diagnosed using spirometry. The technique proposed in this work is based on pre-processing respiratory sounds by enhancing them using specifically designed parametric equalizer filter. Spectro-temporal Gabor filter-bank-based features (GBFB) are extracted from pre-processed respiratory sounds to classify them using different machine learning models. Also, an exhaustive study is performed to determine most efficient features for classifying respiratory sounds. The proposed method is novel and gives state-of-the-art results with an F1 score of 97.95%, precision of 100% and recall of 96%. The method proposed can be used for automated diagnosis of respiratory illness and can circumvent spirometry test in some scenarios. It can even help people living in remote and rural areas where finding an expert to carry out the PFT is limited.

Sex estimation from Thai hand radiographs using convolutional neural networks

Manual analysis of hand radiographs for sex estimation is prone to biases and errors. This study addresses the need for automated methods by exploring the potential of Convolutional Neural Networks (CNNs) to accurately identify individual sex from Thai hand radiographs, overcoming limitations in data availability and variable quality. To improve dataset quality, we applied contrast limited adaptive histogram equalization (CLAHE) and Gaussian blur filter techniques to Thai hand radiographs from 385 male and 788 female individuals. We split these images into training, validation, and test sets. We also applied image augmentation techniques to increase the number of radiographs in the training dataset. Seven CNN models were trained, validated, and evaluated on 100 unseen male and female radiographs each. Among these models, the InceptionResNetV2 architecture demonstrated superior performance, achieving an accuracy of 87.50 % and an F1-Score of 86.91 %. Notably, this model utilized information from the 2nd to the 5th metacarpal bone and proximal phalanges in males, and from the 2nd metacarpal bone in females. Our findings provide a solid foundation for sex estimation from Thai hand radiographs, highlighting the power of CNNs in mitigating challenges associated with data quantity and quality. By automating the sex estimation process using CNNs, forensic analysis can benefit from enhanced accuracy and objectivity, enabling faster and more reliable sex assessment. We envisage that future research will build upon these findings to further improve the performance of sex estimation, contributing to advancements in forensic analysis and facilitating more effective utilization of Thai hand radiographs for sex estimation.

Contrast modification for pre-enhancement process in multi-contrast rubeosis iridis images

Existing researchers for rubeosis iridis disease focused on image enhancement as a collective group without considering the multi-contrast of the images. In this paper, the pre-enhancement process was proposed to improve the quality of iris images for rubeosis iridis disease by separating the image into three groups; low, medium and high contrast. Increment, decrement and maintenance of the images’ original contrast were further operated by noise reduction and multi-contrast manipulation to attain the best contrast value in each category for increased compatibility prior subsequent enhancement. As a result, this study proved that there have three rules for the contrast modification method. Firstly, the histogram equalization (HE) filter and increasing the image contrast by 50% will achieve the optimum value for the low contrast category. Experimental revealed that HE filters successfully increase the luminance value before undergoing the contrast modification method. Secondly, reducing the 50% of the image contrast to achieve the optimum value for the high contrast category. Finally, the image contrast was maintained for the middle contrast category to optimise contrast. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the outputs were then calculated, yielding an average of 18.25 and 28.87, respectively.

Preliminary Analysis on the Effect of Different Denoising Techniques towards Texture Features of MRI Images of Alzheimer’s Disease

Early detection of Alzheimer’s disease (AD) has become one of the major research topics nowadays. The utilization of the computerized system may help medical experts to better understand and analyse the magnetic resonance imaging (MRI) images of AD patients for early detection. One of the commonly steps taken for the analysis of the image is image denoising using certain filters. However, finding shows that previous researchers use different approaches. This study aims to analyse the effect of different denoising techniques towards detection of Alzheimer’s disease. Data of two different groups (AD patients and Normal Control) were collected from Alzheimer's Disease Neuroimaging Initiative (ADNI). Then brain extraction and skull stripping were performed. Several image denoising techniques were implemented for both groups namely median filter, Wiener filter, histogram equalization filter and Gaussian low pass filter. After that, all images underwent texture feature extraction process and analysis were made to see the effect of those denoising techniques towards the features of Gray-Level Co-occurrence Matrix (GLCM) extracted which are the contract, correlation, energy and homogeneity features. The result shows that the use of mentioned denoising filters do not give effect to the extracted features. All values of contrast, correlation, energy and homogeneity cannot clearly distinguish between AD and NC groups. Without any filters on the other hand, contrast feature gives the best output in distinguishing between AD and NC groups with the normalized value of 0.1. The result from this study may help in thorough investigation of other features or hybrid features that could be used for the purpose of detection and classification of AD.

Deep Neural Network-Based Ensemble Model for Eye Diseases Detection and Classification

Fundus images are the principal tool for observing and recognizing a wide range of ophthalmological abnormalities. The automatic and robust methods based on color fundus images are urgently needed since few symptoms are observable in the early stages of the disease. Experts must manually evaluate images to detect diseases for screening procedures to be effective. Due to the complexity of the screening procedure and the shortage of experienced personnel, developing successful screening-based treatments is costly. Although existing automated approaches strive to address these issues, they cannot handle a wide range of diseases and real-world circumstances. We design an automated deep learning-based ensemble method to detect and classify eye diseases from fundus images to address the abovementioned problems. A deep CNN-based model is proposed in the ensemble method that incorporates a mix of 20 layers, including the activation, optimization, and loss functions. The contrast-limited adaptive histogram equalization (CLAHE) and Gaussian filter are utilized in the pre-processing step to get more explicit images and eliminate noise. To avoid overfitting in the training phase, augmentation techniques are applied. Three pre-trained CNN models, including VGG16, DenseNet201, and ResNet50, are employed to compare and assess the efficiency of the proposed CNN model. Experimental results demonstrate that the ensemble approach outperforms recent approaches, which is comparatively state-of-art in the ODIR publicly available dataset.

Analysis of Covid-19 CT chest image classification using Dl4jMlp classifier and Multilayer Perceptron in WEKA Environment.

In recent years, various deep learning algorithms have exhibited remarkable performance in various data-rich applications, like health care, medical imaging, as well as in computer vision. Covid-19, which is a rapidly spreading virus, has affected people of all ages both socially and economically. Early detection of this virus is therefore important in order to prevent its further spread. Covid-19 crisis has also galvanized researchers to adopt various machine learning as well as deep learning techniques in order to combat the pandemic. Lung images can be used in the diagnosis of Covid-19. In this paper, we have analysed the Covid-19 chest CT image classification efficiency using multilayer perceptron with different imaging filters, like edge histogram filter, colour histogram equalization filter, color-layout filter, and Garbo filter in the WEKA environment. The performance of CT image classification has also been compared comprehensively with the deep learning classifier Dl4jMlp. It was observed that the multilayer perceptron with edge histogram filter outperformed other classifiers compared in this paper with 89.6% of correctly classified instances.