Where Have the Litigants Gone?

The recognition of coral species based on underwater texture images pose a significant difficulty for machine learning algorithms, due to the three following challenges embedded in the nature of this data: 1) datasets do not include information about the global structure of the coral; 2) several species of coral have very similar characteristics; and 3) defining the spatial borders between classes is difficult as many corals tend to appear together in groups. For this reason, the classification of coral species has always required an aid from a domain expert. The objective of this paper is to develop an accurate classification model for coral texture images. Current datasets contain a large number of imbalanced classes, while the images are subject to inter-class variation. We have analyzed 1) several Convolutional Neural Network (CNN) architectures, 2) data augmentation techniques and 3) transfer learning. We have achieved the state-of-the art accuracies using different variations of ResNet on the two current coral texture datasets, EILAT and RSMAS.Comment: 22 pages, 10 figure

Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis

Timely detection of surface damages on wind turbine blades is imperative for minimizing downtime and avoiding possible catastrophic structural failures. With recent advances in drone technology, a large number of high-resolution images of wind turbines are routinely acquired and subsequently analyzed by experts to identify imminent damages. Automated analysis of these inspection images with the help of machine learning algorithms can reduce the inspection cost. In this work, we develop a deep learning-based automated damage suggestion system for subsequent analysis of drone inspection images. Experimental results demonstrate that the proposed approach can achieve almost human-level precision in terms of suggested damage location and types on wind turbine blades. We further demonstrate that for relatively small training sets, advanced data augmentation during deep learning training can better generalize the trained model, providing a significant gain in precision

Feasibility analysis of floating photovoltaic power plant in Bangladesh: A case study in Hatirjheel Lake, Dhaka

The installation of large-scale photovoltaic (LSPV) power plants is a solution to mitigate the national energy demand in Bangladesh. However, the land crisis is one of the key challenges for the rapid growth of ground-mounted LSPV plants in Bangladesh. The per unit cost of energy from ground-mounted PV systems is rising as a response to numerous difficulties, particularly for large-scale electricity generation. To overcome the issues with land-based PV, the floating photovoltaic (FPV) could be a viable solution. To the aspirations of the Sustainable and Renewable Energy Development Authority (SREDA), this article has investigated the feasibility of constructing a floating solar plant at Hatirjheel Lake in Dhaka, Bangladesh. The lake is an excellent spot to build an FPV plant due to its geographic location and climatic conditions inside the capital city. In this paper, the design of the plant and tariff are carried out using the PVsyst simulator. It is found that the optimum cost of energy for the plant is $ 0.0959/KWh, which is lesser than the currently operational ground-mounted PV plants in Bangladesh. Additionally, the projected 6.7 MW plant can meet 12.5 % of the local energy demand. Furthermore, the FPV plant is capable to cut off 6685 tons of CO2 annually. A reduction in power costs and environmental protection would assist the government of Bangladesh in achieving the sustainable development goals and electricity generation target of 6000 MW from solar photovoltaics by 2041 as well

Smooth 2D manifold extraction from 3D image stack - Image data

Following Image data from the paper are hosted here - DENDRITES, MEMBRANE,NEURON1,TUBULIN, SYNTHETIC TISSSUE, CANCER CELLS, EPENDYMAL CELLS, NEURON2, NUCLE

A study on local binary pattern for automated weed classification using template matching and support vector machine

Concerns regarding the environmental and economic impacts of excessive herbicide applications in agriculture have promoted interests in seeking alternative weed control strategies. In this context, an automated machine vision system that has the ability to differentiate between broadleaf and grass weeds in digital images to optimize the selection and dosage of herbicides can enhance the profitability and lessen environmental degradation. This paper presents an efficient and effective texture-based weed classification method using local binary pattern (LBP). The objective was to evaluate the feasibility of using micro-level texture patterns to classify weed images into broadleaf and grass categories for real-time selective herbicide applications. Two well-known machine learning methods, template matching and support vector machine, are used for classification. Experiments on 200 sample field images with 100 samples from each category show that, the proposed method is capable of classifying weed images with high accuracy and computational efficiency