Sign language recognition (SLR) is crucial for connecting the deaf and hearing communities. Achieving efficient hand gesture recognition is challenging due to variations in lighting, backgrounds, hand sizes, shapes, and similarities among gestures. To address these challenges, a two-stage framework is proposed. In the first stage, a modified automatic GrabCut is employed to segment the hand region from complex backgrounds, thereby removing unwanted noise. The second stage performs accurate hand gesture classification. An enhanced model named EffiSign, based on EfficientNet-B7, is introduced. This architecture leverages a compound scaling technique to simultaneously optimize depth, width, and resolution. Performance is further improved through fine-tuning by selectively unfreezing layers from specific blocks. EffiSign was evaluated on multiple datasets, including MUGD, NUS-II, ISL, and ArASL, achieving mean accuracies of 97.22

Brain tumor classification from MRI scans is a key process in the early diagnosis and treatment of brain tumors. Currently, various approaches have been proposed, but they have some limitations due to poor feature representation and generalization. In this study, a novel hybrid model, DHFuse-BTC, is proposed. It consists of a preprocessing phase that applies Contrast Enhancement and Dynamically Thresholded Adaptive Denoising. After that, Tomek Links and SMOTE are used to balance the data. Deep learning models, in which optimized Vision Transformers outperform other models in representing spatial dependencies, and handcrafted models, in which Histogram of Oriented Gradients is found to be effective in representing local textures, are used to extract and fused the features. In this study, the proposed model achieved classification accuracies of 97.23%, 97.90%, and 98.35% on figshare CE-MRI, SARTAJ, and Br35H datasets, respectively, outperformed to state-of-the-art models.

Background: Estrus detection by observing behavioral signs are time consuming and ineffective method that causes approximately 21 days loss with a significant financial consequence. This study highlights the value of electrical resistance of vaginal mucus (ERVM) during estrus and its association with genital changes, follicle diameter and first service conception rate (FSCR) in crossbred cows. Methods: A total of 400 cows were selected from different states of North-East, India, out of which 200 numbers of cows were examined to determine the ERVM value prior to artificial insemination (AI) by using Draminski estrus detector.The remaining 200 number of animals were examined for studying the association of ERVM with genital changes, follicle diameter and FSCR.The ERVM value obtained prior to AI was divided into three groups such as 150-200 (Group I), 201-250(Group II) and 251-290 (Group III) ohm, comprising 50 animals in each group. A control groups comprising 50 animals were kept in the study.The genital changes were recorded by transrectal palpation and follicle diameters were recorded by using transrectal ultrasonographic (USG) probe. Result: The mean ERVM value prior to AI was 196.26±1.25 ohm.The lowest ERVM value (150-200 and 201-250 ohm) was closely associated with free flowing vaginal discharge, open cervix, moderate tone of uterus and mature graffian follicle.The follicles diameters were differed significantly between groups. The FSCR was found to be higher when the cows were inseminated at ERVM value from 201-250 ohm. The result suggested that, ERVM value can be consider as a suitable tool for detection of proper time of AI in cows.

Pathologic diagnosis is a critical phase in deciding the optimal treatment procedure for dealing with colorectal cancer (CRC). Colonic polyps, precursors to CRC, can pathologically be classified into two major types: adenomatous (malignant potential) and hyperplastic (benign). Various imaging techniques, such as narrow band imaging (NBI) and white light imaging (WLI), are adopted in capturing polyp-specific features for accurate classification and have different advantages. However, the existing classification techniques mainly rely on a single imaging modality and show limited performance due to data scarcity. Recently, generative artificial intelligence has been gaining prominence in overcoming such issues, especially with various generation-controlling mechanisms using text prompts and images. However, such mechanisms require class labels to make the model respond efficiently to the provided control input. In the colonoscopy domain, such controlling mechanisms are rarely explored; specifically, the text prompt is a completely uninvestigated area. Moreover, the unavailability of expensive class-wise labels for diverse sets of images limits such explorations. This raises the key question of how diverse and clinically meaningful colonoscopy images can be generated in a text-controlled manner from limited annotated data. Therefore, in this work, we develop a novel model, PathoPolyp-Diff, that generates text-controlled synthetic images with diverse characteristics in terms of pathology, imaging modalities, and quality, enabling more effective augmentation of downstream diagnostic models. The proposed model follows a two-stage process: first, the model learns to distinguish polyp from non-polyp characteristics, and then it focuses on pathology-specific features. In the process, we introduce cross-class label learning to make the model learn features from other classes, reducing the burdensome task of data annotation. We validate the effectiveness of text-controlled synthesis and cross-class label learning by performing polyp classification (adenomatous/hyperplastic) with different imaging modalities (NBI/WLI) and text prompts. The experimental results show that incorporating the proposed synthetic images for data augmentation yields an improvement of up to 7.91% in balanced accuracy on a publicly available dataset, highlighting the utility of our approach for enhancing downstream classification performance. Moreover, cross-class label learning achieves a statistically significant improvement of up to 18.33% in balanced accuracy during video-level analysis. The code is available at https://github.com/Vanshali/PathoPolyp-Diff.

Systolic time intervals (STIs), such as pre-ejection period (PEP), left ventricular ejection time (LVET) and PEP-to-LVET ratio, often measured by echocardiography, play vital roles in assessing cardiac contractility, cardiac efficiency, and sympathetic activity. However, to facilitate these measurements for wearables, multiple cardiac signals are needed, resulting in more required sensing devices. In this study, we developed a machine learning (ML) approach to estimate ventricular depolarization (VD) timings using only a single low-cost microelectromechanical system (MEMS)-based accelerometric sensor for measuring the STIs. A cardio-mechanical seismocardiogram (SCG) signal, manifested by rhythmic chest-vibrations, was acquired from the sensor. The SCG signal effectively captures the fiducial points required for measuring LVET. However, computing PEP involves VD timing instants, that are usually identified by the R-peaks of the electrocardiogram (ECG) signal. Consequently, measuring PEP requires more than one cardiac signal. To avoid the use of multiple sensing modalities, this study aims to compute R-peaks instants directly from the SCG signal. We devised a deep feedforward neural network (DFN) with SCG-based features to estimate VD events, which were referenced by R-peaks of concurrent electrocardiogram (ECG) signal. The estimated R-peaks along with the required SCG fiducial points can be used to measure the STIs. A set of self-recorded real-time SCG data collected in supine position was used to train and validate the model. The performance was validated using mean absolute error (MAE), root mean square error (RMSE), and standard deviation of error (SDE). Experimental results showed that the VD events detected using the proposed model has a MAE of 0.080, RMSE of 0.167, and SDE of 0.164. Also, the proposed DFN model was compared with classical ML models, such as random forest and eXtreme Gradient Boosting, where our DFN outperformed all. This demonstrates the standalone utilization of SCG as a cardiac tool for measuring left ventricular systolic functions, offering potential applications in assessing various ventricular anomalies.

After annotating a medical imaging modality that is relatively straightforward to label, doctors often expect automatic annotations for images from other modalities of the same region, even though these modalities differ in contrast and structure. This study focuses on creating automatic lymph node annotation in MRI images using available CT annotations via deep-learning models. Training such models typically requires partial MRI labels for semi-supervision. However, annotating lymph nodes in MRI images is particularly challenging due to their small size and the high cost of MRI scans. These factors make it difficult to create labeled MRI datasets for deep learning model training. Moreover, existing cross-modal annotation methods primarily focus on large tumors and require large datasets, making them unsuitable for small lymph nodes with less training data. We address these challenges using cross-modal supervision through image registration. Our algorithm reduces the burden of manual annotation and the reliance on large labeled datasets and eliminates the need for any MRI ground truth. The algorithm has three steps: 1) unsupervised deformable image translation-based registration of MRI to CT image, producing registered MRI; 2) annotating lymph nodes in registered MRI with the available CT labels; and 3) deregistration of registered annotated MRI back to the original shape of MRI. The translation-based registration model for the algorithm's first and third steps uses a discriminator-free StyleGAN2 translation network and a deformable image registration network with a U-Net-inspired architecture. This registration network includes local and global feature extraction modules, a local-global spatial correlation module, and a superresolution loss function. Our approach eliminates the need for MRI labels by registering MRI with CT images. Experiments show 2.19% and 4.08% MSE reductions, 5.40% and 3.28% SSIM improvements, 29.85% and 3.82% NCC increases for cross-modality and mono-modality registration, respectively, along with a 36.7% training speedup over state-of-the-art translation-based registration models. The lymph node annotation method achieves an average of 74.3% DSC in the region of interest. It also has broader applications in multimodality image segmentation. We open-source the code through a GitHub repository.

Background: Reproductive performance is one of the major determinants for the economic improvement of a dairy farm. Endometritis and other uterine complications cause decrease the pregnancy rate. In this study, the genital changes and haemato-biochemical profile of crossbred dairy cows affected with endometritis was studied to evolve a suitable treatment protocol in order to improve reproductive efficiency. Methods: Crossbred cow affected with endometritis were selected based on mucopurulent vaginal discharge at estrus and tested positive for white side test. Animals were divided randomly into 8 groups with 24 animals in each group. Different therapeutic regimens fortified with supportive treatment were opted. The therapeutic regimens were supportive therapy having bypass fat, vitamins and minerals, Lugol’s iodine, Lugol’s iodine with supportive therapy, intrauterine (IU) antibiotic, IU antibiotic with supportive therapy, E. coli. LPS, E. coli. LPS with supportive therapy and control group respectively. Efficacy of each treatment regimen was based on first service conception rate (FSCR). Result: Of all the therapeutic regimens, fortification of Lugol’s iodine with supportive therapy resulted in higher FSCR (83.33%) indicating better applicability as a treatment tool for endometritis in crossbred cows.

According to the World Health Organization, the stroke burden is rising in the world. Timely quantification of stroke severity is essential to improve stroke outcomes. Recently, deep learning-based segmentation algorithms have emerged to aid clinicians. However, deep learning techniques require a substantial amount of data for training. Earlier studies encountered challenges with insufficient data for network training. To address this challenge, we developed a model incorporating an intra-domain transfer learning (intra-DTL) framework for multiple modalities and the channel-spatial attention (CSA) module for effective feature extraction. This intra-DTL employs a bottleneck-based multimodal framework that leverages similar data from the same domain. This method integrates common (present in both datasets) and uncommon (not present in both datasets) modalities to enhance feature extraction, enabling effective knowledge transfer. Moreover, the CSA module employs a squeeze-and-excitation module, and region-specific global attention (RSGA) is utilized collectively to enhance the features. Additionally, we incorporated a class-balanced approach during training with the source dataset. Further, we also analyzed the effect of groups in RSGA, different frameworks for effective knowledge transfer, and weight variations analysis. The experiments are carried out on the ISLES 2015 dataset. These experimental results demonstrate that incorporating a bottleneck-based intra-DTL framework, attention, and a class-balanced training approach improved results, achieving a mean Dice score of 0.837. These findings highlight the effectiveness of the intra-DTL framework for multimodal input in transferring knowledge from the source to the target dataset.

Background: The study on testes of local dog of Assam is of great value in regard to germplasm conservation. The aim of the study was to evaluate the gross and histomorphological examination of testes of male reproductive system. Methods: The testes were collected at the time of castration from Department of Surgery and Radiology, College of Veterinary Science, Assam Agricultural University, Khanapra, Guwahati, Assam, India. The research was carried out for a period of one year in Department of Anatomy and Histology, College of Veterinary Science, Assam Agricultural University, Khanapara, Guwahati, Assam. Then gross anatomical studies were made on it and the tissue samples were fixed in 10% neutral buffered formalin solution and were processed as per the standard technique of procedure (Luna, 1968). The paraffin blocks were sectioned in Shandon Finesse microtome at 5 µm thickness and the sections were stained with Mayer’s Haematoxylin and Eosin staining technique for Cellular details as per the method of Luna (1968). Result: Grossly, the testes of local dog consisted of two surface viz., lateral and medial and two ends i.e. upper end and lower end. The upper end of the testes was occupied by the head of the epididymis and the lower end of the testes was occupied by the tail of the epididymis. Mediastinum testis was observed in the centre of testes of local dog. Histologically, the testes were covered by serous layer (Tunica vaginalis), connective tissue layer (Tunica albugenia) and vascular layer (Tunica vasculosa) from outside to inwards. Spermatogenic cells like spermatogonia, primary spermatocytes, secondary spermatocytes, spermatids and spermatozoa, and sertoli cells were observed in seminiferous tubules. The sertoli cells were attached to the basement membrane of seminiferous tubules. Cluster of Leyding cells were found between the semineniferous tubules and it contained large spherical nuclei. The epididymides were lined by pseudo stratified ciliated columnar epithelium.

Brain tumor segmentation from multimodal MRI scans is still a hard and crucial problem in medical imaging, and the outcome directly affects diagnosis, treatment plan, and patient prognosis. Current deep learning models like U-Net and its traditional variations tend to be limited in detecting fine-grained tumor boundaries and modeling multi-scale contextual information, especially when dealing with heterogeneous tumor structure and contrast-poor areas. These shortcomings result in poor segmentation performance, particularly in defining intricate tumor areas. To remedy these issues, we introduced MDS-ResUNet, an improved U-Net-inspired architecture that is particularly designed to enhance brain tumor segmentation precision. The model presented a few major innovations: (1) multi-scale residual learning, which enabled hierarchical feature aggregation across decoder levels to maintain semantic coherence and spatial resolution; (2) an atrous pyramid pooling (APP) module, enabling multi-scale contextual feature extraction by dilated convolutions, strengthening the model to detect tumors with different sizes and morphologies; (3) an edge-aware module (EAM), enabling tumor boundary defining by promoting edge feature representations explicitly; and (4) deep supervision, enforced on multiple decoder levels to encourage stable gradient flow and efficient multi-scale feature learning. Experimental verification was performed on the BraTS19 dataset, wherein MDS-ResUNet showed better performance with Dice values of 0.946 (Whole Tumor), 0.903 (Tumor Core), and 0.843 (Enhancing Tumor). Competitive IoU, sensitivity, and specificity measures were also reported. Through the proper resolution of the shortcomings of existing methods, MDS-ResUNet created a new state of the art in brain tumor segmentation, showing promising future applications in both deep learning research and clinical use.

As super-resolution techniques continue to evolve, there is a growing requirement for more advanced methods to capture finer details, particularly when dealing with the smaller pixels within an image. In remote sensing, enhanced spatial details can find utility in diverse applications, such as disaster management, urban planning, and environmental change detection. Many existing image super-resolution algorithms are there to improve image resolution. However, they are not explicitly crafted to accommodate the distinctive attributes of remote-sensing images, rendering them less effective in restoring the details of the images. Therefore, we proposed a convolutional block attention residual network with joint adversarial mechanisms (CRNJAM) to capture finer details in remote sensing images. We first designed a generator based on the residual network and attention mechanism. This has the ability to produce high-quality images with superior resolution, even when the input is of low quality. Then, we train the super-resolved images with high-resolution images with the help of two types of discriminators to generate more realistic images. The first discriminator evaluates an input sample’s local regions or patches. On the other hand, the second discriminator evaluates the entire input sample as a whole. The result shows that the proposed model can significantly reduce the noise in the generated super-resolved image; also, the SR image generated using the proposed method provides competitive advantages over the images generated using other models.

Video stitching is crucial in multi-camera-based systems that provide 360-degree surveillance and monitoring applications. Homography estimation is the most important step in video/image stitching. All the existing methods mainly focus on homography estimation through traditional keypoint detection or a more recent deep learning approach. These estimation methods are primarily based on multiple homography calculations and fail for featureless images, which lack keypoints, such as the plain sky. To overcome these limitations, we propose a novel real-time video stitching method based on homography estimation through camera calibration using fixed camera configuration. As the proposed method is based on the relative position of two cameras, the overall image/video stitching process is independent of the world scene. In addition, our method calculates the homography matrix only once during the video stitching process. Thus reducing the per-frame stitching time by about 30

Colonoscopy video acquisition has been tremendously increased for retrospective analysis, comprehensive inspection, and detection of polyps to diagnose colorectal cancer (CRC). However, extracting meaningful clinical information from colonoscopy videos requires an enormous amount of reviewing time, which burdens the surgeons considerably. To reduce the manual efforts, we propose a first end-to-end automated multi-stage deep learning framework to extract an adequate number of clinically significant frames, i.e., keyframes from colonoscopy videos. The proposed framework comprises multiple stages that employ different deep learning models to select keyframes, which are high-quality, non-redundant polyp frames capturing multi-views of polyps. In one of the stages of our framework, we also propose a novel multi-scale attention-based model, YcOLOn, for polyp localization, which generates ROI and prediction scores crucial for obtaining keyframes. We further designed a GUI application to navigate through different stages. Extensive evaluation in real-world scenarios involving patient-wise and cross-dataset validations shows the efficacy of the proposed approach. The framework removes 96.3% and 94.02% frames, reduces detection processing time by 38.28% and 59.99%, and increases mAP by 2% and 5% on the SUN database and the CVC-VideoClinicDB, respectively. The source code is available at https://github.com/Vanshali/KeyframeExtraction Note to Practitioners-The widespread acceptance of colonoscopy procedures as a gold standard for CRC screening is constrained by the massive amount of data recorded during the process that needs to be manually reviewed. Such manual procedures are burdensome and induce human diagnostic errors. This article suggests an automated framework to extract keyframes (important frames) from colonoscopy videos that can efficiently represent the clinically relevant information captured in the video streams. This is achieved by the automated removal of uninformative and highly correlated frames, which do not add to clinical findings. The approach ensures diversity among keyframes and provides clinicians with a multi-view of polyps for easy resection. In addition, the proposed multi-scale attention-based model improves the polyp localization performance, which further helps in refining the keyframe selection process. The comprehensive experimental results corroborate that discarding insignificant frames can enhance polyp detection and localization performance and reduce computational requirements. The study estimates 30% to 60% time saving for clinicians during video screening. In clinical practices, the proposed automated framework and our designed GUI would enable surgeons to visualize the essential data better with minimal manual interventions and assist in precise polyp resection.

This article deals with the measurement of the hand keypoints in a vision-based setup under different constraints. Hand keypoint detection (HKD) plays a crucial role in many gesture-based applications. However, developing a generalized detection method has remained a long-standing problem. Several factors impede accurate detection: the fingers' distance from the camera and their nearness, self-occlusion, variations in illumination, and background clutter. To overcome these barriers, we propose a two-stage architecture. The first stage generates precise hand regions, eliminating adjoining skin regions and background clutter. The second stage incorporates a novel multiscale attention block to detect keypoint coordinates precisely. Qualitative and quantitative evaluations found that the proposed architecture outperforms state-of-the-art models, with endpoint errors as low as 2.3, 1.14, and 2.11 pixels for the three benchmark datasets. This advancement lays the groundwork for future 3-D hand pose estimation developments and their applications.

In semantic segmentation, an input image is partitioned into multiple meaningful segments each corresponding to a specific object or region. Multi-scale context plays a vital role in the accurate recognition of objects of different sizes and hence is key to overall accuracy enhancement. To achieve this goal, we introduce a novel strategy called Distributed Multi-scale Pyramid Pooling (DMPP) to extract multi-scale context at multiple levels of feature hierarchy. More specifically, we employ Pyramid Pooling Modules (PPM) in a distributed fashion after all three stages during the encoding phase. This enhances the feature representation capability of the network and leads to better performance. To extract context at a more granular level, we propose an Efficient Multi-scale Context Aggregation (EMCA) module which uses a combination of small and large kernels with large and small dilation rates, respectively. This alleviates the problem of sparse sampling and leads to consistent recognition of different regions. Apart from model accuracy, small model size and efficient execution are critically important for real-time mobile applications. To achieve it, we employ a resource-friendly combination of depthwise and factorized convolutions in the EMCA module to drastically reduce the number of parameters without significantly compromising the accuracy. Based on the EMCA module and DMPP, we propose a lightweight and real-time Distributed Multi-scale Pyramid Network (DMPNet) that achieves an excellent accuracy-efficiency trade-off. We also conducted extensive experiments on both driving datasets (i.e., Cityscapes and CamVid) and a general-purpose dataset (i.e., ADE20K) to show the effectiveness of the proposed method.

For the hard-of-hearing population, communicating medical issues to doctors who do not understand sign language can be challenging. To address this problem, researchers have focused extensively on sign language gesture recognition. However, existing methods often struggle with conversion time and the accuracy of recognizing similar gestures. In this paper, we propose a sign language gesture recognition system utilizing the Yolov9 model. The system's performance was evaluated using a publicly available Kaggle dataset, achieving an impressive Mean Average Precision (mAP@O.5) of 99.5%. Experimental results show that the proposed method surpasses state-of-the-art techniques in both efficiency and accuracy.

Background: Study on the reproductive health of dogs is a matter of concern since it affects the existence of future generation. There are various reproductive ailments that may affect a dog in its lifetime. The present study was aimed to find out the incidence of different reproductive disorders in canine populations reported at the Veterinary Clinical Complex (VCC), College of Veterinary Science, Guwahati, India in respect of breed, age, sex and season. Methods: The study was conducted on canine populations reported at the VCC, College of Veterinary Science, Guwahati, India during the period from 1/03/2016 to 28/02/2021. Clinical cases were grouped based on their breed, age, sex and the season and the type of various reproductive ailments and their incidence was worked out. Result: The overall incidence of reproductive disorders in dogs was 2.45%. Labradors had the maximum incidence of reproductive ailments (24.79%) followed by local breeds (17.52%) and the least was in Dachshund (2.56%). The animals under the age group 1.1 to 4.0 years appeared to have the highest incidence (47.86%) of reproductive ailments and the lowest (12.39%) was in 10.1 to 13.0 years of age group. The highest incidence of reproductive ailments was recorded in female dogs (96.15%). In canine population, summer resulted in higher (35.86%) incidence of reproductive disorders.

Rectal cancer remains to be a major issue worldwide, necessitating accurate and precise methods for proper classification of lymph nodes in MRI scan images to guide treatment decisions. The use of deep learning based techniques can help with the task of classification. This research focuses on enhancing the process of lymph node classification in rectal cancer MRI images by using a multi-model approach. Firstly, we explore the use of diffusion models in data augmentation, to produce synthetic lymph node images to counter the issue of limited dataset size. Secondly, Real-ESRGAN and Swin2SR super-resolution techniques are examined for their ability to improve image quality and hence enhance the accuracy of lymph node classifications. Lastly, ConvNeXt is employed as an advanced technique used to improve overall performance at classifying lymph nodes for rectal cancer. Through a series of detailed experiments on the dataset consisting of T2-Weighted Axial MRI scans from 54 patients, we demonstrate the effectiveness of our approach in classification of lymph nodes. The metrics like accuracy and precision exhibit significant improvements, highlighting the superiority of our multi-model approach.

Background: The interaction between nutrition and reproduction has long been known to have important implications for the reproductive performance in animal.The present study highlights the effect of by-pass fat supplementation on genital changes and blood biochemical profile in pubertal swamp buffalo heifers and puerperal swamp buffalo cows. Methods: 24 heifers of 2-2.5 years of age and 24 puerperal cows on the day of parturition, irrespective of parity were used as experimental animal. The analytical method was based on the treatment protocols of 3 groups in both heifers and cows (n=8 in each group) viz., treated with oral bypass fat alone (I), oral bypass fat, mineral mixtures and injectable phosphorus (II) and control (III) respectively. Genital status, metabolic hormone (leptin, Ghrelin and IGF-1), reproductive hormone (estrogen and progesterone) and mineral constituents (calcium, phosphorus, zinc and copper) were analyzed on day 0, day 15 and day 30 in heifers and day 15, day 30 and day 45 in cows respectively. Result: Feeding of bypass fat alone or bypass fat fortified with minerals and injectable phosphorus was not found to induce significant genital changes in heifers and cows. The serum leptin and IGF-1 levels were increased significantly (p less than 0.05) in heifers at day 30 in Group I and II. However, a significant rise (p less than 0.05) of leptin was recorded at day 45 in group I and II cows. IG1-1 level was recorded increased significantly only in cows treated with fortified bypass fat (Group II). With the progression of treatment the serum phosphorus level was increased significantly (p less than 0.05) in Group I and II cows. Serum estrogen levels were increased in both heifers and cows with progression of treatment in both Group I and II. However, serum progesterone levels were increased significantly only in heifers of Group I and II with progression of treatment.

A dimensionality reduction technique based on singular value decomposition (SVD) is proposed for the aberration and spurious fringe removal from the phase measurement in off-axis digital holographic microscopy. The SVD of complex-valued virtual image obtained from numerically reconstructed hologram is computed. The phase aberration and spurious fringe compensated phase estimate is obtained by appropriate selection of singular values to reconstruct the denoised phase image. In order to remove the artifacts created along the x and y axis due to the SVD, the filtering procedure is implemented by rotating the phase image a fixed number of times. The effective denoised image is obtained by the weighted combination of denoised image evaluated for each rotation. Simulation and experimental studies are conducted to demonstrate the practical applicability of the proposed method.