On vortical disturbances in single and two-fluid boundary layers

The vortical disturbance environment surrounding a laminar boundary layer affects the proceedings of transition to turbulence. The exponentially growing Tollmien–Schlichting wave is observed under low free-stream turbulence levels, and is replaced by the algebraically growing streaks upon further increase in the turbulence intensity. This scenario is significantly affected by the presence of wall films which introduce additional instabilities and alter the amplification of the streaks. In this work, the effect of wall films on the linear stability of boundary layers is investigated using the Orr-Sommerfeld, Squire and the interface displacement equations. A modal analysis is conducted first, in order to identify all the unstable modes and their respective regimes of dominance. Furthermore, the physical mechanisms contributing to disturbance growth are studied using the kinetic energy equation. No unstable eigenvalues are found for wall films less viscous than the outer stream. Under such conditions, the streaks are likely to dominate; their amplification being dependent on the penetration of the free-stream vortical disturbances into the boundary layer. The ingestion of the free-stream vorticity in the mean shear is explained using the continuous spectrum of the Orr-Sommerfeld equation. A unique parameter is identified to distinguish three asymptotic regimes representing complete, partial and negligible penetration into the boundary layer, respectively. The physical mechanism is a competition between viscous diffusion and convection by the mean flow. The wall film affects the penetration into the boundary layer by modifying the wall-normal wavenumber across the interface. The penetrating free-stream disturbances efficiently generate streaks by tilting the mean vorticity. Their amplification is investigated using an initial value problem that describes the evolution of a linear perturbation. Lower viscosity wall films reduce the amplification of the streaks. However, another growth mechanism arising from the interfacial displacement dominates at long time and is enhanced for lower viscosity films

Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild

Automatic Perceptual Image Quality Assessment is a challenging problem that impacts billions of internet, and social media users daily. To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupervised setting. The unique novelty of our approach is its ability to generate low-level representations of image quality that are complementary to high-level features representing image content. We refer to the framework used to train the two encoders as Re-IQA. For Image Quality Assessment in the Wild, we deploy the complementary low and high-level image representations obtained from the Re-IQA framework to train a linear regression model, which is used to map the image representations to the ground truth quality scores, refer Figure 1. Our method achieves state-of-the-art performance on multiple large-scale image quality assessment databases containing both real and synthetic distortions, demonstrating how deep neural networks can be trained in an unsupervised setting to produce perceptually relevant representations. We conclude from our experiments that the low and high-level features obtained are indeed complementary and positively impact the performance of the linear regressor. A public release of all the codes associated with this work will be made available on GitHub.Comment: Accepted to IEEE/CVF CVPR 2023. Code will be released post conference in July 2023. Avinab Saha & Sandeep Mishra contributed equally to this wor

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

Arduino Based Automatic Irrigation System

In the present days, the farmers are suffering from severe drought like condition throughout the year. The main objective of this paper is to provide a system leads to automatic irrigation thereby saving time, money & power of the farmers, gardeners in greenhouses etc. Manual intervention is common in traditional farm-land irrigation techniques. This paper presents a technique for Arduino based Automatic Irrigation System. With this automated technology of irrigation, human intervention can be minimized. The moisture sensors will be bedded in on the field. Whenever there is a change in water concentration, these sensors will sense the change and gives an interrupt signal to the microcontroller. Soil is one of the most fragile resources whose soil pH property used to describe the degree of the acidity or basicity, which affects nutrient availability and ultimately plant growth. Thus, the system will provide automation, remote controlling and increased efficiency. The humidity sensor is connected to internal ports of microcontroller via comparator; whenever there is a fluctuation in temperature and humidity of the environment, these sensors sense the change in temperature and humidity and give an interrupt signal to the micro-controller and thus the motor is activated. A buzzer is used to indicate that the pump is on