One of the important indicators of cryospheric transitions in the Arctic is the formation of meltponds on sea ice, and much of the interest in these features is in the context of climate change. The scarcity of annotated arctic sea ice data is a major challenge in training a deep learning model for the prediction of the dynamics of the melt ponds. We use a diffusion model, a class of generative models, to generate synthetic arctic sea ice data for further analysis of meltponds. Based on the training data, diffusion models can generate new and realistic data that are not present in the original dataset by focusing on the data distribution from a simple to a more complex distribution. First, the simple distribution is transformed into a complex distribution by adding noise, such as a Gaussian distribution and through a series of invertible operations. Once trained, the model can generate new samples by starting from a simple distribution and diffusing it to the complex distribution, capturing the underlying features of the data. During inference, when generating new samples, the conditioning information is provided as input alongside the starting noise vector. This guides the diffusion process to produce samples that adhere to the specified conditions. We used high-resolution aerial photographs of the Arctic region obtained during the Healy-Oden Trans Arctic Expedition (HOTRAX) in 2005 and NASA's Operation IceBridge DMS L1B Geolocated and Orthorectified data acquired in 2016 for the initial training of the generative model. The original image and synthetic image are assessed based on their chromatic similarity. We employed an evaluation metric, the Chromatic Similarity Index (CSI) for these assessments.

With increasing global temperatures due to anthropogenic climate change, seasonal sea ice in the Arctic has experienced rapid retreat, with increasing areal extent of meltponds that occur on the surface of retreating sea ice. Because meltponds have a much lower albedo than sea ice or snow, more solar radiation is absorbed by the underlying water, further accelerating the melting rate of sea ice. However, the dynamic nature of meltponds, which exhibit complex shapes and boundaries, makes manual analysis of their effects on underlying light and water temperatures tedious and taxing. Several classical image processing approaches have been extensively used for the detection of meltpond regions in the Arctic area. We propose a Convolutional Neural Network (CNN) based multiclass segmentation model termed NABLA-N (del(N)) for automated detection and segmentation of meltponds. The architectural framework of NABLA-N consists of an encoding unit and multiple decoding units that decode from several latent spaces. The fusion of multiple feature spaces in the decoding units enables better representation of features due to the combination of low and high-level feature maps. The proposed model is evaluated on high-resolution aerial photographs of Arctic sea ice obtained during the Healy-Oden Trans Arctic Expedition (HOTRAX) in 2005 and NASA's Operation IceBridge DMS L1B Geolocated and Orthorectified image data in 2016. These images are classified into three classes: meltpond, open water and sea ice. We determined that NABLA-N demonstrates superior performance on segmentation of meltpond data compared to other state-of-the-art networks such as UNet and Recurrent Residual UNet (R2UNet).

The event camera is researched, developed, and designed to imitate the human eye; it is a groundbreaking vision sensor with the following advantages over a standard camera: a net rate that is much faster, a latency that is far less, a high dynamic range, and it uses far less power. These fundamental properties assist in enabling the design of third-generation algorithms in Spiking Neural Networks intended to mimic the human brain and vision processing. Moreover, these fundamental properties enable swift robotics despite the challenges of motion blur and high latency that standard cameras face. Also, these properties should enable motion estimation from the surface features on Mars, which is difficult to achieve with standard cameras. For this research, the team is using a NASA Space use-case application. For this NASA Space application, the team has chosen a challenging motion-estimation task involving a Mars-based above-ground Helicopter beyond “Ingenuity” and the planet and surface of Mars. Event-based cameras have been gaining interest within the computer vision community. They are particularly suitable for applications with challenging temporal constraints and safety requirements. Thus, Event-based sensors are an excellent match for Spiking Neural Networks (SNNs), as coupling an asynchronous sensor with neuromorphic hardware can result in real-time systems with minimal power requirements. Moreover, methods to verify and validate event-based sensing platforms for space applications are lacking. In this paper, we investigate the addition of fuzzy logic models to arrive at an explainable SNN algorithm for the NASA space use-case. Ultimately, the team aims to develop a unified model yielding reasonably accurate optical flow estimates.

Wildfires are a key aspect of many ecosystems, but climate change has created conditions more conducive for devastating wildfires. Thus, it is imperative that relevant agencies know where small fires occur expeditiously. Remote sensing is a key tool for active fire detection (AFD), and satellite imagery in particular is useful due to covering wide areas. Semantic segmentation architectures like U-Net have been used for AFD and have proven very effective. In this paper, we apply a unique variant of U-Net called ResWnet towards AFD, using a large global dataset. ResWnet achieved a precision of 95% and an F-Score of 94.2%, which is better than a U-Net trained on the same dataset.

The use of deep learning is particularly effective for biomedical applications involving semantic segmentation. In semantic segmentation, one of the most popular deep learning architectures is U-Net, which is specifically designed for feature cascading for pixel classification. There are several versions of U-Net, such as Residual U-Net (ResU-Net), Recurrent U-Net (RU-Net), and Recurrent Residual U-Net (R2U-Net), which have been proposed for improved performance. The recurrent connection in a layer of the neural network can create a cycle of transferring the output information of a layer back to itself as an input. Each layer's output responses can thus be thought of as additional input variables. The new model is based on Residues in Succession U-Net where the residues from successive layers extract reinforced information from the previous layers in addition to the recurrent feedback loop exhibiting several advantages. The improved learning and accumulation of the features in subsequent layers play a major part. The proposed model produces precise extraction and accumulation of features from each layer reinforcing the learning. The outputs of the combination of recurrent and residues in successive layers ensure better feature representation for segmentation tasks. We use a benchmark expert-annotated dataset viz. Structured Analysis of Retina (STARE) for measuring the abilities of the Residues in Succession Recurrent U-Net (RSR U-Net) to segment blood vessels in retinal images. The testing and evaluation results show that the new model provides improved performance when compared to U-Net, R2U-Net and Residues in Succession U-Net in the same experimentation setup.

The massive shift in temperatures in the Arctic region has caused the increased Albedo effect as higher amount of solar energy is absorbed in the darker surface due to melting ice and snow. This continuous regional warming results in further melting of glaciers and loss of sea ice. Arctic melt ponds are important indicators of Arctic climate change. High-resolution aerial photographs are invaluable for identifying different sea ice features and are great source for validating, tuning, and improving climate models. Due to the complex shapes and unpredictable boundaries of melt ponds, it is extremely tedious, taxing, and time-consuming to manually analyze these remote sensing data that lead to the need for automatizing the technique. Deep learning is a powerful tool for semantic segmentation, and one of the most popular deep learning architectures for feature cascading and effective pixel classification is the UNet architecture. We introduce an automatic and robust technique to predict the bounding boxes for melt ponds using a Multiclass Recurrent Residual UNet (R2UNet) with UNet as a base model. R2UNet mainly consists of two important components in the architecture namely residual connection and recurrent block in each layer. The residual learning approach prevents vanishing gradients in deep networks by introducing shortcut connections, and the recurrent block, which provides a feedback connection in a loop, allows outputs of a layer to be influenced by subsequent inputs to the same layer. The algorithm is evaluated on Healy-Oden Trans Arctic Expedition (HO-TRAX) dataset containing melt ponds obtained during helicopter photography flights between 5 August and 30 September 2005. The testing and evaluation results show that R2UNet provides improved and superior performance when compared to UNet, Residual UNet (Res-UNet) and Recurrent U-Net (R-UNet).

The reduction of energy losses is one of the most critical problems faced by electric distribution companies. There are numerous studies available for evaluating technical energy losses in distribution networks. Due to the large impact of load change during normal operation time, load factor and loss factor variables are used in the loss determination technique. This research examines the effect of repairing fault period on the estimation of technical losses for medium voltage feeders in a ring distribution network. To underline this effect, comparison between technical losses calculations utilizing the regular operation period alone, the normal operation period and repairing fault period are made. For this purpose, simulation is carried out using the “Electrical Transient Analysis Program (ETAP)” software, which is used to assess power losses in both conditions including the system's normal operating conditions and in condition of fault. Technical losses calculating methods include a proposed component to represent the impact of the repairing fault duration. A proposed formula based on the number of hours spent fixing faults over the course of the year is used to compute the new factor. Real data and measurements from an actual ring distribution network are used to assess the performance of suggested technique. According to simulation and computational results, ignoring the repairing fault period during the calculation process may produce inaccurate values.

We propose a modified U-Net architecture incorporating the residues from successive layers for the extraction of features in subsequent layers. The new Residues in Succession U-Net model is evaluated for blood vessel segmentation in retinal images on a benchmark expert-annotated dataset viz. Structured Analysis of Retina (STARE). The testing and evaluation results shows improved performance when compared to U-Net and R2U-Net in the same experimentation setup. A nonlinear image enhancement strategy is employed to improve the fine details in the images so that the network will be able to capture more information in further processing.

In this Project, the torsion viscometer is safe designed to determine fluid viscosity. It is supported by a steel base and column. The components of the torsion viscometer are designed, fabricated and assembled followed by calibrating it to obtain the experimental results using standard fluids. The calibrated torsion viscometer is being used to test various oils like SAE 20W 40, SAE 15W 40, SAE 80W 90 and SAE 50 in the present study. The angle of extra swing is obtained at different temperature for which the Redwood seconds are obtained from the standardized graph. The experimental values of viscosity are compared with theoretical values and error is found. It is observed that for all the oils tested, the obtained values of viscosity are more accurate in the temperature varying from 40 degrees C to 50 degrees C. It is also observed that it can be used to measure viscosity of thick oils only and not thin oils.