Satellite image denoising is essential for enhancing the visual quality of images and for facilitating further image processing and analysis tasks. Designing of self-tunable 2-D finite-impulse response (FIR) filters attracted researchers to explore its usefulness in various domains. Furthermore, 2-D FIR Wiener filters which estimate the desired signal using its statistical parameters became a standard method employed for signal restoration applications. In this paper, we propose a 2-D FIR Wiener filter driven by the adaptive cuckoo search (ACS) algorithm for denoising multispectral satellite images contaminated with the Gaussian noise of different variance levels. The ACS algorithm is proposed to optimize the Wiener weights for obtaining the best possible estimate of the desired uncorrupted image. Quantitative and qualitative comparisons are conducted with 10 recent denoising algorithms prominently used in the remote-sensing domain to substantiate the performance and computational capability of the proposed ACSWF. The tested data set included satellite images procured from various sources, such as Satpalda Geospatial Services, Satellite Imaging Corporation, and National Aeronautics and Space Administration. The stability analysis and study of convergence characteristics are also performed, which revealed the possibility of extending the ACSWF for real-time applications as well.

Artificial intelligence and computer vision have widespread applications in workout analysis. It has been extensively used in sports and the athlete industry to identify errors and improve performance. Furthermore, these methods prevent injuries caused by a lack of instructors or costly infrastructure. One such exercise is the squat, which is a movement in which a standing person descends to a posture with their torso vertical and their knees firmly bent, then returns to their original upright position. Each person’s squat is distinct, with varying limb lengths causing their form to change when observed. It has been observed that the mobility of various joints and muscular strength have a role in this. A squat improves the user by increasing overall leg strength, strengthening knee and hip joints, and lowering the risk of heart disease due to cardiovascular development. This paper presents a method for classifying squat types and recommending the right squat version. This study uses MediaPipe and a deep learning-based technique to decide if squatting is good or bad. A stacked Bidirectional Gated Recurrent Unit (Bi-GRU) model with an attention layer is proposed to consistently and fairly assess each user, categorizing squats into seven classes. This stacked Bi-GRU model with an attention unit is then compared to other cutting-edge models, both with and without the attention layer. The model outperforms other models by attaining an accuracy of 94% and is demonstrated to work the best and most consistently for our dataset. Furthermore, the individual executing the incorrect squat is corrected to the best of their ability, depending on their performance and body proportions, by providing the correct form.

In the recent years, researchers are quite much attracted in designing two-dimensional (2-D) adaptive finite-impulse response (FIR) filters driven by an optimization algorithm to self-adjust the filter coefficients, with applications in different domains of research. For signal processing applications, FIR Wiener filters are commonly used for noisy signal restorations by computing the statistical estimates of the unknown signal. In this paper, a novel 2-D Cuckoo search adaptive Wiener filtering algorithm (2D-CSAWF) is proposed for the denoising of satellite images contaminated with Gaussian noise. Till date, study based on 2-D adaptive Wiener filtering driven by metaheuristic algorithms was not found in the literature to the best of our knowledge. Comparisons are made with the most studied and recent 2-D adaptive noise filtering algorithms, so as to analyze the performance and computational efficiency of the proposed algorithm. We have also included comparisons with recent adaptive metaheuristic algorithms used for satellite image denoising to ensure a fair comparison. All the algorithms are tested on the same satellite image dataset, for denoising images corrupted with three different Gaussian noise variance levels. The experimental results reveal that the proposed novel 2D-CSAWF algorithm outperforms others both quantitatively and qualitatively. Investigations were also carried out to examine the stability and computational efficiency of the proposed algorithm in denoising satellite images.

Fiber-reinforced polymer (FRP) composites undergo different environmental conditions in the designed life span. An investigation on aging of FRPs in water helps to enhance the material’s durability. This article aims to explore the effect of different types of water soaking (viz., tap, sea, and rain water) and re-drying conditions on the tensile and flexural strengths of glass fiber-epoxy composites (GFEC). The prepared specimens are placed in the sea, tap, and rain water for 10 weeks. Some specimens are re-dried at 50°C for 5 weeks. The water uptake (%) of seawater aged specimens decreases compared to tap and rain water-aged specimens at the end of 10 weeks. Water soaking of composites has reduced the tensile and flexural strengths of GFEC by 14 to 17%. The strength of water-soaked composite specimens is partly retrieved in specimens that have been re-dried. More than 90% of the original value is retrieved for the strength of GFECs (as-made specimens). SEM analysis of the tensile specimen fracture surface reveals the causes of the specimen failure as well as the impact of water soaking, followed with re-drying.

Haze can significantly reduce visibility and contrast of images captured outdoors, necessitating the enhancement of images. This degradation in image quality can adversely affect various applications, including autonomous driving, object detection, and surveillance, where poor visibility may lead to navigation errors and obscure crucial details. Existing dehazing techniques face several challenges: spatial methods tend to be computationally heavy, transform methods often fall short in quality, hybrid methods can be intricate and demanding, and deep learning methods require extensive datasets and computational power. To overcome these challenges, we present ODD-Net, a hybrid deep learning architecture. Our research introduces a comprehensive data set and an innovative architecture called Atmospheric Light Net (A-Net) to estimate atmospheric light, using dilated convolution, batch normalisation, and ReLU activation functions. Furthermore, we develop T-Net to measure information transmission from objects to the camera, using multiscale convolutions and nonlinear regression to create a transmission map. The integrated architecture combines pre-trained A-Net and T-Net models within the atmospheric scattering model. Comparative analysis shows that ODD-Net provides superior dehazing quality, especially in transmission map estimation and depth measurement, surpassing state-of-the-art methods such as DCP, GMAN, DehazeNet, and LCA. Our quantitative analysis reveals that ODD-Net achieves the highest performance in terms the quality metrics compared. The proposed method demonstrates notable quantitative and qualitative improvements over existing techniques, setting a new standard in image dehazing.