Hardware In The Loop Implementation and Simulations of a Wave Energy Converter using Typhoon HIL and Speedgoat

Several studies have indicated that electricity production is the prime source of greenhouse emissions, more than flying and driving combined. Hence, over the past few decades, there has been a significant rise in the need for clean energy to achieve the targets in emissions reduction. It has been observed that the share of renewable energy sources in the percentage of total electricity production is rising, but, according to climate experts, the transition needs to speed up. Hence, it is significant to study and develop underutilized renewable energy sources. One of these underutilized renewable energy sources is wave energy. It has been observed that wave energy is a source with very high potential and predictability but has been in the nascent development stage for a while now due to several technical challenges. One of them is the limitation in studying all the real-world scenarios in a laboratory and the cost of up-scaling it to the real world. A low-cost, efficient solution to study and validate such technologies is Hardware In the Loop (HIL) implementation. In this study, an attempt has been made to develop a systematic wave energy converter (WEC) model using Typhoon HIL and Speedgoat. The developed model can act as a universal model to pick, study, test, and validate the individual components of the WEC in the HIL environment. Preliminary results show a reasonable trend in the behavior of the WEC. WEC system behavior for both single and multi-frequency wave tests has been discussed in the report. The stability/operating conditions for the current model configuration and a few insights on the energy storage system have also been discussed

Domain Aligned Prefix Averaging for Domain Generalization in Abstractive Summarization

Domain generalization is hitherto an underexplored area applied in abstractive summarization. Moreover, most existing works on domain generalization have sophisticated training algorithms. In this paper, we propose a lightweight, weight averaging based, Domain Aligned Prefix Averaging approach to domain generalization for abstractive summarization. Given a number of source domains, our method first trains a prefix for each one of them. These source prefixes generate summaries for a small number of target domain documents. The similarity of the generated summaries to their corresponding documents is used for calculating weights required to average source prefixes. In DAPA, prefix tuning allows for lightweight finetuning, and weight averaging allows for the computationally efficient addition of new source domains. When evaluated on four diverse summarization domains, DAPA shows comparable or better performance against the baselines, demonstrating the effectiveness of its prefix averaging scheme.Comment: 13 pages, Accepted to ACL 2023 Finding

GETT-QA: Graph Embedding based T2T Transformer for Knowledge Graph Question Answering

In this work, we present an end-to-end Knowledge Graph Question Answering (KGQA) system named GETT-QA. GETT-QA uses T5, a popular text-to-text pre-trained language model. The model takes a question in natural language as input and produces a simpler form of the intended SPARQL query. In the simpler form, the model does not directly produce entity and relation IDs. Instead, it produces corresponding entity and relation labels. The labels are grounded to KG entity and relation IDs in a subsequent step. To further improve the results, we instruct the model to produce a truncated version of the KG embedding for each entity. The truncated KG embedding enables a finer search for disambiguation purposes. We find that T5 is able to learn the truncated KG embeddings without any change of loss function, improving KGQA performance. As a result, we report strong results for LC-QuAD 2.0 and SimpleQuestions-Wikidata datasets on end-to-end KGQA over Wikidata.Comment: 16 pages single column format accepted at ESWC 2023 research trac

The Role of Output Vocabulary in T2T LMs for SPARQL Semantic Parsing

In this work, we analyse the role of output vocabulary for text-to-text (T2T) models on the task of SPARQL semantic parsing. We perform experiments within the the context of knowledge graph question answering (KGQA), where the task is to convert questions in natural language to the SPARQL query language. We observe that the query vocabulary is distinct from human vocabulary. Language Models (LMs) are pre-dominantly trained for human language tasks, and hence, if the query vocabulary is replaced with a vocabulary more attuned to the LM tokenizer, the performance of models may improve. We carry out carefully selected vocabulary substitutions on the queries and find absolute gains in the range of 17% on the GrailQA dataset.Comment: Accepted as a short paper to ACL 2023 finding

Malayalam Handwritten Character Recognition using CNN Architecture

The process of encoding an input text image into a machine-readable format is called optical character recognition (OCR). The difference in characteristics of each language makes it difficult to develop a universal method that will have high accuracy for all languages. A method that produces good results for one language may not necessarily produce the same results for another language. OCR for printed characters is easier than handwritten characters because of the uniformity that exists in printed characters. While conventional methods find it hard to improve the existing methods, Convolutional Neural Networks (CNN) has shown drastic improvement in classification and recognition of other languages. However, there is no OCR model using CNN for Malayalam characters. Our proposed system uses a new CNN architecture for feature extraction and softmax layer for classification of characters. This eliminates manual designing of features that is used in the conventional methods. P-ARTS Kayyezhuthu dataset is used for training the CNN and an accuracy of 99.75% is obtained for the testing dataset meanwhile a collection of 40 real time input images yielded an accuracy of 95%

Influence of resource gradients and habitat edges on density variation in tiger populations in a multi-use landscape

Spatial heterogeneity in the local densities of terrestrial carnivores is driven by multiple interacting biotic and abiotic factors. Space-use patterns of large carnivores reflect the competing demands of resource selection (e.g., exploitation of habitats with abundant prey) and minimization of risks arising from human interactions. Estimating the relative strength of these drivers is essential to understand spatial variation in densities of large carnivores and there are still key knowledge gaps for many large carnivore populations. To better understand the relative roles of environmental and human drivers of spatial variation in tiger (Panthera tigris) densities, we surveyed a 3000 km2 landscape in North India using camera trap data. Over two years, we photo-captured 92 unique adult tigers. Associating spatial covariates with patterns of detection allowed us to test hypotheses about the relative influence of prey abundance, habitat structure and extent, and proximity to habitat edges on spatial variation in tiger densities across a gradient of anthropogenic disturbance. We documented extensive variation in tiger density within and across management units and protected areas. Spatial variation in prey abundance and proximity to grassland habitats, rather than human use (e.g. extent of human-dominated edge habitat and protection status), explained most of the spatial variation in tiger density in two of the five surveyed sites. The region’s largest tiger population occurred in a multi-use forest beyond protected area boundaries, where wild ungulates were abundant. Our results suggest that tigers can occur at high densities in areas with extensive human use, provided sufficiently high prey densities, and tracts of refuge habitats (eg. areas with dense vegetation with low human use). We argue that tiger conservation portfolio can be expanded across multi-use landscapes with a focus on areas that are adaptively managed as “zones of coexistence” and “refuge habitats”. Advancing this conservation strategy is contingent on greatly strengthening systems to effectively and equitably redress human–wildlife conflict and leveraging existing policies to strengthen local participation in conservation planning and forest stewardship. Our insights into the environmental drivers of spatial heterogeneity in tiger populations can inform both local management and guide to species recovery in working landscapes

Lookie here! Designing interventional user interfaces for conditional self-driving vehicles

In this paper we investigated whether providing directional alerts to a user’s active screen can augment their ability to regain situational awareness when traveling in a conditional autonomous (Level 3) vehicle. A user study (N=15) was conducted in the lab environment with a driving simulator, where users were distracted by playing a game on a mobile device. A non-directional alert was compared to two separate directional alerts: the central user interface (UI) and the peripheral UI. One located at the center, and one located at the periphery of the participant’s vision while they were focused on the mobile device screen, to understand whether direction data can assist the user. Although there were no significant differences in reaction times, participants perceived themselves performing better when provided with directional alerts. Our findings imply that directional user interfaces have the potential to reduce overall cognitive load and lead to better user experiences for passengers of self-driving vehicles.M.S

The Extent of Private participation in European Mission Oriented-innovation Policy: An exploratory analysis of the CORDIS database

The advent of the 21st century has seen economies worldwide increasingly investing towards developing smart and sustainable innovations for addressing social and environmental challenges such as climate change, adapting to demographic change, public health care an well-being etc. These challenges, often referred to as 'grand challenges' are extremely complex in nature and require dynamic collaborations between the state and private actors to explore and exploit new areas of growth, and develop suitable solutions for the same. Through mission-oriented innovation, policy makers provide a framework for systematically implementing mission-led research to bring together the willing public and private actors to create system-wide transformations across the entire value chain.By virtue of its abundance of knowledge, skills and resources, the European Union provides a fervent ground for implementing mission-oriented innovation to address grand challenges, but this fragmentation could also make it an extremely complex scenario to do the same. However, through public funds such as the Horizon 2020 framework programme, the public sector can provide a foundation for initiating mission-oriented policies by funding the early, high-risk and uncertain stages of innovation, which private organizations and SMEs can capitalize on to develop smart innovations. In order to do so, the public sector must possess the appropriate set of dynamic capabilities for bringing private actors to actively work towards developing solutions for addressing grand challenges. Additionally, the fragmentation of knowledge and skills available across member nations of the European Union, industrial sectors or technologies differ, making it highly probable that the degree of private participants in mission-oriented innovation also differs.This thesis identifies the extent of private participation in European mission-oriented innovation by qualitatively analyzing data extracted from the European Commission's Community Research and Development Information Service (CORDIS) database. A descriptive statistical analysis of the data extracted from CORDIS has identified the current degree of private participation in initiatives encompasses in the Horizon 2020 framework programme. This study identifies the proportion of private participation in the various member states of the European Union and across different societal challenges. It also, identifies the participation of SMEs and incumbent in these initiatives. Based on the results obtained, this study discusses its implications for mission-oriented innovation and provides a scope for possible areas for future studies. Additionally, this study goes one step further to analyze the CORDIS database to understand it benefits and shortcomings.Lastly, this study also provides recommendations for steering policies in a more effective to better suit actors/researchers/policy makers from specific regions or sectors. The results can be utilized by researchers to conduct studies to identify the dependent variables that directly affect the participation of private actors, which can open up areas of research to understand what factors drive the participation of actors in mission-oriented policies. It also provides insights on the possibilities of using the CORDIS database for steering mission-oriented research.Management of Technology (MoT

Design and development of Image processing algorithms for quantitative road traffic data analysis

The project aims to design and develop efficient algorithms using image processing techniques for the quantitative analysis of road traffic data in addition to enabling a better learning experience of a more commonly used software MATLAB. Road traffic data has been a major concern for traffic engineers in optimizing the efficiency and capacity of any modern transport system. The project aspires to develop a real time traffic analysis system for monitoring traffic flow, collect statistical data of traffic analysis and enhance the alogrithms in place to attain higher efficiency. Several existing traffic monitoring techniques such as edge detection, background difference, and inter-frame difference among others were researched and implemented in this project. Traffic video samples from express highways as well as city roads were collected for different lighting conditions, extracted into frames and subjected to different image processing techniques in MATLAB. Existing algorithms and Fuzzy Logic algorithms were implemented to obtain quantitative data such as vehicle speed and vehicle count and a comparative analysis was performed to obtain the better algorithm and better technique. Vehicle classification as per the size was incorporated in addition to measuring the percentage of road usage. The implementation and processing was done by designing a MATLAB Graphical User Interface by keeping in mind a myriad of possible user defined settings. Results comparison between the segmentation techniques showed that edge detection was the better method. In addition, a comparative study was done to observe which angle of video footage gave better results – the front angle or back angle. Also, comparison of the different lighting conditions was performed. A study of the results obtained when using different frame extraction rate, various window detection lengths was also done. In addition, a comparison of the results of the traffic in express highways and city roads was performed to observe if the results were aligned.Bachelor of Engineerin

Anti-Asthmatic Effects of Saffron Extract and Salbutamol in an Ovalbumin-Induced Airway Model of Allergic Asthma

Introduction: Asthma is a chronic inflammatory disorder of the airways often characterized by airway remodeling and influx of inflammatory cells into the airways. Saffron (C. sativus) has been reported to possess anti-inflammatory, anti-allergic and immunomodulatory properties. Salbutamol is known to relax airway smooth muscles. Objective: To investigate the combined anti-asthmatic effect of C. sativus extract (CSE) and salbutamol in an ovalbumin (OVA)-induced asthma in rats. Materials and methods: Airway hyperresponsiveness (AHR) was induced in male Sprague-Dawley rats by OVA challenge and treated with CSE (30 mg/kg and 60 mg/kg i.p.) and salbutamol (0.5 mg/kg p.o) for 28 days. After the induction period, various hematological, biochemical, molecular (ELISA) and histological analyses were performed. Results: OVA-induced alterations observed in hematological parameters (total and differential cell counts observed in Bronchoalveolar Lavage Fluid (BALF) were significantly attenuated (p p p < 0.01) increase in OVA induced Th2 cytokine levels (TNF-α, IL-1β, IL-4, IL-13). Histopathological analysis of lung tissue showed that combined effect of CSE and salbutamol treatment ameliorated OVA-induced inflammatory influx and ultrastructural aberrations. Conclusion: The results obtained from this study show that the combined effect of CSE and salbutamol exhibited anti-asthmatic properties via its anti-inflammatory effect and by alleviating Th2 mediated immune response. Thus, this treatment combination could be considered as a new therapeutic strategy for management of asthma