Water causes degradation of quality in optical images captured underwater due to its physical properties of absorption and scattering. This degradation is further aggravated by the increase in water depth and the presence of contaminated water. Transformers in the vision domain have made a quantum leap in many vision tasks such as detection, and segmentation but yet to make any progress in enhancing degraded underwater images. We propose a transformer-based model named “Aquaformer” which makes four major contributions: an adaptive layer normalization, replacement of masked cyclic shift with symmetric padding in window partitioning, a novel aggregation mechanism, and an adjustable fusion approach. These succeed in making the model a very powerful one, producing significantly better performance compared to the latest state-of-the-art methods. Testing on multiple benchmark datasets, employing both quantitative and qualitative metrics, establishes its supremacy.

In the past few decades, due to rapid growth in industrialization, there has been a steady decline of the air quality along with an increase in the concentration of PM2.5. It is well known that a high PM2.5 concentration adversely affects the environment and has hazardous impact on public health. Therefore, it is important to monitor the PM2.5 concentration at geographic locations where air quality monitoring stations are presently unavailable, especially in remote areas. Unfortunately, installation of such monitoring stations requires expensive instruments and constant maintenance. This paper presents a novel, low-cost and portable alternative to such measurement apparatus, where PM2.5 concentration is estimated based on image input obtained from a camera. The novelty of the present work lies in its hitherto unique attempt to capture information regarding PM2.5 content from visibility degradation caused by the pollutant which is further supplemented by important knowledge regarding seasonal and diurnal variation of it. The latter has a crucial role in the prevention of confounding effects arising from the presence of other weather and atmospheric elements. Another important highlight is the use of a full reference image metric as a feature, for which a powerful, dehazing algorithm has been employed. The results obtained are extremely promising, providing a close to accurate estimation of PM2.5 concentration with R2values far higher than reported in the literature. To summarize, the construction of a unique feature set, together with an appropriate machine learning algorithm, lead to an extremely reliable, stand-alone approach, deployable on a hand-held device such as a mobile and is a very significant contribution indeed of the proposed approach.

Optimal location queries identify the best locations to set up new facilities for providing service to its users. For several businesses such as fuel stations, cellphone base-stations, etc., placement queries require taking into account the mobility patterns (or trajectories) of the users. In this work, we formulate the TOPS (Trajectory-Aware Optimal Placement of Services) query that locates the best k sites on a road network for the prevailing user trajectories. The problem is NP-hard. The greedy approach, which is the state-of-the-art technique for this problem, is not scalable and practical for real urban-scale scenarios, primarily due to its high memory footprint beyond the capabilities of commodity machines. To overcome these challenges, we develop an indexing framework called NETCLUS that derives its power through an unique combination of FM sketches with network clustering. Empirical studies show that NETCLUS requires less than 100 s to answer the TOPS query on real datasets comprising of more than 250,000 sites and 120,000 trajectories.