Critical analysis of classification techniques for polarimetric synthetic aperture radar data

Full polarimetry SAR data known as PolSAR contains information in terms of microwave energy backscattered through different scattering mechanisms (surface-, double- and volume-scattering) by the targets on the surface of land. These scattering mechanisms information is different in different features. Similarly, different classifiers have different capabilities as far as identification of the targets corresponding to these scattering mechanisms. Extraction of different features and the role of classifier are important for the purpose of identifying which feature is the most suitable with which classifier for land cover classification. Selection of suitable features and their combinations have always been an active area of research for the development of advanced classification algorithms. Fully polarimetric data has its own advantages because its different channels give special scattering feature for various land cover. Therefore, first hand statistics HH, HV and VV of PolSAR data along with their ratios and linear combinations should be investigated for exploring their importance vis-à-vis relevant classifier for land management at the global scale. It has been observed that individually first hand statistics yield low accuracies. And their ratios are also not improving the results either. However, improved accuracies are achieved when these natural features are stacked together

PROACTIVE EXCHANGE OF DATA BETWEEN CLOUD PROVIDERS VIA CONTROLLER COORDINATION AND TRIGGER DYNAMIC WORKFLOWS

A multi-cloud Software Defined Network (SDN) controller proactively learns insights about subscribers, such as enterprise users, end users, and/or other cloud providers. Based on the learned insights, the multi-SDN controller applies dynamic policies on other cloud provides to which those subscribers are attached to. The multi-cloud SDN controller co-ordinates with various cloud providers, enterprise network controllers, and Internet Service Providers (ISPs) to proactively notify other cloud providers with information about affected users so that those providers can install additional resources at cloud edge/core on the fly. Additionally, the multi-cloud SDN controller facilitates a warm hand off from one cloud region to another cloud region. When the multi-cloud SDN controller learns about an enterprise outage, it proactively notifies other cloud providers of the outage event and the other cloud providers can use this for a warm hand off of session to the region(s) through which the users will be reconnected. The likely regions are derived based on telemetry obtained from multi-cloud SDN controller. The multi-cloud SDN controller also triggers a proactive cleanup of user context of the cloud provider side. The cloud provider cleans up after the connection reset event based on information from the multi-cloud SDN controller, rather than wait on a timeout of the connection

Assessing the factors impacting shipping container dwell time: A multi-port optimization study

Ocean transportation is the most preferred mode of transportation that represents a significant role in the global trade. Ocean transportation comprises around 80% of the aggregate worldwide cargo volume. This research paper focused on evaluating the factors that influence the dwell time of the shipping containers. Dwell time is one of the important port performance parameters which evaluates the time spent by the container in a port. In this research, the data from the fourteen major ports was collected and analysed across the variables, such as cycle, size, mode, status, delivery and tracking technology for evaluating the variation in container dwell time. OLS regression method (Ordinary least squares) along with independent sample T test was adopted for the analysis of 2.8 million container data entries utilizing python for big data analysis and SPSS. For the top three ports with lowest RMSE (Root mean square error), Port A – 15.6 %, Port G – 15.7 % and Port L – 15.86 %, a qualitative study was performed to identify the reasons for the variation in dwell time. The major reasons identified included free days period, trans-shipment port, high rail frequency, industrial hubs in the vicinity of the ports for lower dwell time. A qualitative research framework was presented as the research outcomes and reasons for variations in a multiport study.FaME TBU, (IGA/FaME/2022/005

Assessing the factors impacting shipping container dwell time: a multi-port optimization study

Ocean transportation is the most preferred mode of transportation that represents a significant role in the global trade. Ocean transportation comprises around 80% of the aggregate worldwide cargo volume. This research paper focused on evaluating the factors that influence the dwell time of the shipping containers. Dwell time is one of the important port performance parameters which evaluates the time spent by the container in a port. In this research, the data from the fourteen major ports was collected and analysed across the variables, such as cycle, size, mode, status, delivery and tracking technology for evaluating the variation in container dwell time. OLS regression method (Ordinary least squares) along with independent sample T test was adopted for the analysis of 2.8 million container data entries utilizing python for big data analysis and SPSS. For the top three ports with lowest RMSE (Root mean square error), Port A – 15.6 %, Port G – 15.7 % and Port L – 15.86 %, a qualitative study was performed to identify the reasons for the variation in dwell time. The major reasons identified included free days period, trans-shipment port, high rail frequency, industrial hubs in the vicinity of the ports for lower dwell time. A qualitative research framework was presented as the research outcomes and reasons for variations in a multiport study

DYNAMIC TELEMETRY PROFILE ENFORCEMENT IN A CONTROLLER NETWORK

Because telemetry processing can involve high resource usage, such processing is typically provided via a cloud infrastructure. However, there are drawbacks to current implementations involving such cloud infrastructure processing. For example, such processing typically follows standard processing patterns. Yet, with the increasing complexity of different network use cases, there are scenarios that would benefit from dynamic telemetry processing. Presented herein are techniques through which multiple device telemetry profiles can allow a cloud controller to dynamically match a telemetry profile to specific conditions for a tenant network. Each telemetry profile may include selections for data processing through priority and secured queues. Additionally, the cloud controller may have reverse telemetry policies to push reverse telemetry to the customer edge when original usage telemetry data is retrieved, processed, and/or transferred

ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks

The capability of the self-attention mechanism to model the long-range dependencies has catapulted its deployment in vision models. Unlike convolution operators, self-attention offers infinite receptive field and enables compute-efficient modeling of global dependencies. However, the existing state-of-the-art attention mechanisms incur high compute and/or parameter overheads, and hence unfit for compact convolutional neural networks (CNNs). In this work, we propose a simple yet effective "Ultra-Lightweight Subspace Attention Mechanism" (ULSAM), which infers different attention maps for each feature map subspace. We argue that leaning separate attention maps for each feature subspace enables multi-scale and multi-frequency feature representation, which is more desirable for fine-grained image classification. Our method of subspace attention is orthogonal and complementary to the existing state-of-the-arts attention mechanisms used in vision models. ULSAM is end-to-end trainable and can be deployed as a plug-and-play module in the pre-existing compact CNNs. Notably, our work is the first attempt that uses a subspace attention mechanism to increase the efficiency of compact CNNs. To show the efficacy of ULSAM, we perform experiments with MobileNet-V1 and MobileNet-V2 as backbone architectures on ImageNet-1K and three fine-grained image classification datasets. We achieve $\approx$13% and $\approx$25% reduction in both the FLOPs and parameter counts of MobileNet-V2 with a 0.27% and more than 1% improvement in top-1 accuracy on the ImageNet-1K and fine-grained image classification datasets (respectively). Code and trained models are available at https://github.com/Nandan91/ULSAM.Comment: Accepted as a conference paper in 2020 IEEE Winter Conference on Applications of Computer Vision (WACV

RATIONALITY BEHIND AYURVEDA COMPOUND FORMULATIONS- A BIRDS EYE VIEW

Ayurveda is Indian heritage system of medicine gifted by ancient Acharya. It provides scientific approach for dealing human health issues with tools of nature like herbs, minerals, metals etc. It states that every substance in the universe can be applied as medicine with the help of Yukti or logical approach of physicians. In present era, whole world is looking towards Ayurveda for its novel natural healing modalities to get relief from their ailments whether physical or mental. Hence here is the need for development of more numbers of Ayurveda formulations to overcome the different health hazard. Moreover invention of more formulations for newly developed diseases like cancer, AIDs, dengue etc. is also needed. But these herbal preparations also face problems like adulteration, non-availability in a particular area or extinction of herbs due to excessive use of a particular herb. On this background present study was undertaken to analyse the fundamental rationality behind Ayurveda formulations mentioned in various ancient transcripts. Literary data regarding evolving a formulation was scrutinized with examples of important formulations mentioned in various texts. This study results out that for developing a particular formulation, factors like availability, palatability, potency, safety, efficacy etc. should be considered.

Critical analysis of classification techniques for polarimetric synthetic aperture radar data

Full polarimetry SAR data known as PolSAR contains information in terms of microwave energy backscattered through different scattering mechanisms (surface-, double- and volume-scattering) by the targets on the surface of land. These scattering mechanisms information is different in different features. Similarly, different classifiers have different capabilities as far as identification of the targets corresponding to these scattering mechanisms. Extraction of different features and the role of classifier are important for the purpose of identifying which feature is the most suitable with which classifier for land cover classification. Selection of suitable features and their combinations have always been an active area of research for the development of advanced classification algorithms. Fully polarimetric data has its own advantages because its different channels give special scattering feature for various land cover. Therefore, first hand statistics HH, HV and VV of PolSAR data along with their ratios and linear combinations should be investigated for exploring their importance vis-à-vis relevant classifier for land management at the global scale. It has been observed that individually first hand statistics yield low accuracies. And their ratios are also not improving the results either. However, improved accuracies are achieved when these natural features are stacked together

PLY WISE FAILURE ANALYSIS OF MONO LEAF SPRING USING HYBRID C-GFRP COMPOSITES

Composite materials are a better alternative for Leaf spring material in automobiles since they have higher stiffness, high impact energy absorption, lesser stresses and also higher strength to weight ratio. The objective is to study the ply wise failure criteria in the composite leaf springs. Leaf springs are modeled and analyzed using ACP PrePost and studied for failure criteria based on four failure theories which are: maximum stress failure theory, maximum strain failure theory, Tsai-Hill failure theory and Tsai-Wu failure theory. Failure load based on these theories is calculated by conducting a parametric study. To improve the maximum failure load, hybrid composites are designed and analyzed by replacing the top, bottom and center layers of the composite laminate. The four different cross-sections which are analyzed are Eglass/epoxy, HC1, HC2 and HC3. The study shows that replacing the top, bottom and center layers does improve the maximum failure load. Although this introduces higher stresses in the component, the stresses in the Eglass/epoxy material at the same positions from the center of the laminate are reduced. HC3 shows 30.7% increment in failure load by considering only vertical loads and 20.8% increment in failure load by considering vertical, side loads and twist moment simultaneously. There is an agreeable error of 1.44 – 1.65% in the results obtained for deformation and 0.88 – 1.33% for failure load between simulation and theoretical calculations. Mechanical properties of the Eglass/epoxy material are evaluated by conducting tensile test and three-point bending test. Mono leaf spring similar to the dimensions of Maruthi 800 vehicle is made using hand layup method. The load vs deformation results of leaf spring show a good agreement between the experimental and the simulation values

Quantifying the Impact of Digital Transformation on Economic Growth: A Longitudinal Analysis

This study examined how digital transformation and economic development interact in a longitudinal analysis that went from 2016 to 2020. A persistent commitment to digitalization was shown by the statistics, which showed a constant growth in digital transformation measures including investment, adoption rates, talent development, and preparedness. Indicators of economic development, including GDP growth, employment, productivity, and corporate investments, all showed positive trends at the same time. The percentages of the calculated digital transformation impact indicated an increasing effect over time. An examination conducted over a period of five years highlighted the overall effect of digital transformation and emphasized its ongoing contribution to economic development. Policymakers, companies, and academics should take note of this research's important implications, which underscore the pivotal role that digital transformation plays in determining economic advancement in the digital era