Abhishek Samanta

This paper introduces IMPECT-POSE, an innovative front-end and back-end architecture designed to enhance fitness and sports training through precise body posture tracking. This system integrates advanced computer vision and artificial intelligence in pose estimation to provide real-time feedback on exercise execution, which is crucial for maintaining proper technique, reducing injury risks, and optimizing training outcomes. Our evaluations, conducted at two distinct locations with multiple participants, demonstrate the system’s capability to improve exercise performance significantly. The system’s flexibility allows sports professionals to monitor and guide clients remotely, enhancing the accessibility and effectiveness of training regimens. This research highlights the potential of augmented intelligence in transforming sports training, offering a scalable and effective alternative to conventional methods, and paving the way for future advancements in AI-driven personalized training programs. The continued development of this technology aims to refine its accuracy, broaden its applicability to diverse user preferences, and extend its use in practical, real-world settings.

Image retrieval under generalized test scenarios has gained significant momentum in literature, and the recently proposed protocol of Universal Cross-domain Retrieval is a pioneer in this direction. A common practice in any such generalized classification or retrieval algorithm is to exploit samples from many domains during training to learn a domain-invariant representation of data. Such criterion is often restrictive, and thus in this work, for the first time, we explore the generalized retrieval problem in a data-efficient manner. Specifically, we aim to generalize any pre-trained cross-domain retrieval network towards any unknown query domain/category, by means of adapting the model on the test data leveraging self-supervised learning techniques. Toward that goal, we explored different self-supervised loss functions~(for example, RotNet, JigSaw, Barlow Twins, etc.) and analyze their effectiveness for the same. Extensive experiments demonstrate the proposed approach is simple, easy to implement, and effective in handling data-efficient UCDR.

Image denoising is one of the most classic problems in computer vision for restoring corrupted images. It has been approached by using various traditional state of the art architectures in convolutional neural network (CNN), which has demonstrated considerably better results than the prior methods. There has been recent advancements in approaching the problem using generative adversarial networks (GAN), which has shown considerable promise. In this paper, we propose a novel denoising adversarial architecture to generate denoised image samples from a noisy distribution. A denoising autoencoder has been employed as the Generator to learn image distributions and generate denoised images while the discriminator penalizes the generated output. We employ an additive loss comprising of root mean square and mean absolute error for the Generator function. The model is trained adversarially followed by extensive experiments. We achieved PSNR and SSIM values comparable to the state‐of‐the‐art for a range of blind and non‐blind Gaussian noise.

Rapid climate changes with global warming are the primary dispute of the recent era cause of diesel engine emission. Among the probable fuels, biodiesel is the most promising one as the alternative of petroleum diesel. Biodiesel can be formed from residue of cooking oil by transesterification. The reaction rate can be increased with alkali Catalyst at a 55 °C temperature. The actions of waste cooking oil biodiesel (WCOB) compared with petroleum diesel were performed to observe the nature of an unaltered stationary diesel engine. In this study the specific fuels are: B10 biodiesel-diesel blend contained 10% WCOB and petroleum diesel (B0).The performance of an unaltered diesel engine fuelled with WCOB blend is examined in this study. In order to find out the performance results constant engine speed (1500 rpm) is being maintained during the experiments under 0%, 25%, 50% and 100% load conditions. It has been observed that when the engine setup was fuelled with WCOB blend B10, the performance depleted at mid to top load conditions but no drastic reduction in emission.

We define and construct a novel pseudorandom tool, the Markovian hitter. Given an input sequence of n independent random bits, a Markovian hitter produces a sequence of pseudorandom samples in {0, 1}^k, in an online fashion, that hits any subset W ⊂ {0, 1}^k of size ∊2^k with probability ≈ 1 – 2^{–(n–k)∊}. This is comparable to the behavior of truly random samples or classical pseudorandom hitting sets. A Markovian hitter has an additional “Markovian” property of interest: each pseudorandom sample is a function of only the O(k) most recent bits of the input sequence (of random bits). Such Markovian properties are useful in distributed online settings. In particular, we apply Markovian hitters to obtain a new algorithm for the well-studied blind rendezvous problem for cognitive radios. This is the problem faced by two parties equipped with radios that can access channels in potentially different subsets, S₁ and S₂, of a universe of n channels. Their challenge is to discover each other (by tuning their radios to the same channel at the same time) as quickly as possible. In prior work [3] it was shown that deterministic schedules have a lower bound for rendezvous time of Ω(|S₁| · |S₂|). We beat this quadratic barrier by utilizing a public source of randomness in conjunction with a Markovian hitter to achieve rendezvous in expected time We counterbalance this result by establishing two lower bounds on expected rendezvous time: an bound for the setting with public randomness, and an Ω(|S₁| · |S₂|) bound in the setting with private randomness but no public randomness, which is a strengthening of the result for deterministic schedules.