Perturbation Propagation Models for Underwater Sensor Localisation using Semidefinite Programming

Real time Underwater sensor networks (UWSNs) suffer from localisation issues due to a dearth of incorporation of different geometric scenarios in UWSN scenarios. To address these issues, this paper visualises three specific scenarios of perturbation. First, small sized and large numbered particles of perturbance moving in a tangential motion to the sensor nodes; second, a single numbered and large-sized particle moving in a rectilinear motion by displacing the sensor nodes into sideward and forward direction, and third, a radially outward propagating perturbance to observe the influenced sensor nodes as the perturbance moves outwards. A novel target localisation and tracking is facilitated by including marine vehicle navigation as a source of perturbation. Using semidefinite programming, the proposed perturbation models minimise localisation errors, thereby enhancing physical security of underwater sensor nodes. By leveraging the spin, cleaving motion and radial cast-away behaviour of underwater sensor nodes, the results confirm that the proposed propagation models can be conveniently applied to real time target detection and estimation of underwater target nodes

A probabilistic framework for source localization in anisotropic composite using transfer learning based multi-fidelity physics informed neural network (mfPINN)

The practical application of data-driven frameworks like deep neural network in acoustic emission (AE) source localization is impeded due to the collection of significant clean data from the field. The utility of the such framework is governed by data collected from the site and/or laboratory experiment. The noise, experimental cost and time consuming in the collection of data further worsen the scenario. To address the issue, this work proposes to use a novel multi-fidelity physics-informed neural network (mfPINN). The proposed framework is best suited for the problems like AE source detection, where the governing physics is known in an approximate sense (low-fidelity model), and one has access to only sparse data measured from the experiment (highfidelity data). This work further extends the governing equation of AE source detection to the probabilistic framework to account for the uncertainty that lies in the sensor measurement. The mfPINN fuses the data-driven and physics-informed deep learning architectures using transfer learning. The results obtained from the data-driven artificial neural network (ANN) and physicsinformed neural network (PINN) are also presented to illustrate the requirement of a multifidelity framework using transfer learning. In the presence of measurement uncertainties, the proposed method is verified with an experimental procedure that contains the carbon-fiberreinforced polymer (CFRP) composite panel instrumented with a sparse array of piezoelectric transducers. The results conclude that the proposed technique based on a probabilistic framework can provide a reliable estimation of AE source location with confidence intervals by taking measurement uncertainties into account

Augmented Reality and GPS-Based Resource Efficient Navigation System for Outdoor Environments: Integrating Device Camera, Sensors, and Storage

Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and render the mapping information. This system can perform map localisation, pathfinding, and visualisation using a low-power mobile device. A case study was undertaken to evaluate the proposed system. It was observed that the proposed system resulted in a 29 percent decrease in CPU load and a 35 percent drop in memory requirements. As spatial information was stored as comma-separated values, it required almost negligible storage space compared to traditional spatial databases. The proposed navigation system attained a maximum accuracy of 99 percent with a root mean square error value of 0.113 and a minimum accuracy of 96 percent with a corresponding root mean square value of 0.17

Contribution of gender towards open source software: A preliminary study

Open Source Software(OSS)innovation process has become a prominent phenomenon on how software is developed.Yet, gender issues in software industry seem to be duplicated in OSS innovation process.This paper discusses preliminary findings to address the lacuna in the area of OSS innovation process and gender.The study is guided by Social Construction of Technology (SCOT) theory and Feminist theory.This study offer insights for OSS community, not only the benefit towards gender and minorities but familiarizing them with the dynamics, issues and challenges related to OSS innovation thus enhanced their understanding of gender’s and minorities’ contribution in OSS innovation

SERVICE-BASED AUTOMATION OF SOFTWARE CONSTRUCTION ACTIVITIES

The reuse of software units, such as classes, components and services require professional knowledge to be performed. Today a multiplicity of different software unit technologies, supporting tools, and related activities used in reuse processes exist. Each of these relevant reuse elements may also include a high number of variations and may differ in the level and quality of necessary reuse knowledge. In such an environment of increasing variations and, therefore, an increasing need for knowledge, software engineers must obtain such knowledge to be able to perform software unit reuse activities. Today many different reuse activities exist for a software unit. Some typical knowledge intensive activities are: transformation, integration, and deployment. In addition to the problem of the amount of knowledge required for such activities, other difficulties also exist. The global industrial environment makes it challenging to identify sources of, and access to, knowledge. Typically, such sources (e.g., repositories) are made to search and retrieve information about software unitsand not about the required reuse activity knowledge for a special unit. Additionally, the knowledge has to be learned by inexperienced software engineers and, therefore, to be interpreted. This interpretation may lead to variations in the reuse result and can differ from the estimated result of the knowledge creator. This makes it difficult to exchange knowledge between software engineers or global teams. Additionally, the reuse results of reuse activities have to be repeatable and sustainable. In such a scenario, the knowledge about software reuse activities has to be exchanged without the above mentioned problems by an inexperienced software engineer. The literature shows a lack of techniques to store and subsequently distribute relevant reuse activity knowledge among software engineers. The central aim of this thesis is to enable inexperienced software engineers to use knowledge required to perform reuse activities without experiencing the aforementioned problems. The reuse activities: transformation, integration, and deployment, have been selected as the foundation for the research. Based on the construction level of handling a software unit, these activities are called Software Construction Activities (SCAcs) throughout the research. To achieve the aim, specialised software construction activity models have been created and combined with an abstract software unit model. As a result, different SCAc knowledge is described and combined with different software unit artefacts needed by the SCAcs. Additionally, the management (e.g., the execution of an SCAc) will be provided in a service-oriented environment. Because of the focus on reuse activities, an approach which avoids changing the knowledge level of software engineers and the abstraction view on software units and activities, the object of the investigation differs from other approaches which aim to solve the insufficient reuse activity knowledge problem. The research devised novel abstraction models to describe SCAcs as knowledge models related to the relevant information of software units. The models and the focused environment have been created using standard technologies. As a result, these were realised easily in a real world environment. Softwareengineers were able to perform single SCAcs without having previously acquired the necessary knowledge. The risk of failing reuse decreases because single activities can be performed. The analysis of the research results is based on a case study. An example of a reuse environmenthas been created and tested in a case study to prove the operational capability of the approach. The main result of the research is a proven concept enabling inexperienced software engineers to reuse software units by reusing SCAcs. The research shows the reduction in time for reuse and a decrease of learning effort is significant

Influence of government policies on industry development: The case of India's automotive industry

The automotive industry in India has come a long way from its nascent state at the time of India's independence in 1947 to its present day dynamic form. As compared to the production of mere 4,000 vehicles in 1950, the production of the industry crossed the historic landmark of 10 million vehicles in 2006. Today, the industry produces a wide range of automobiles and auto-components catering to both the domestic as well as foreign markets. The development of the industry has been shaped by the demand on the one hand and the government interventions on the other; the influence of the latter being considerable. The evolution of India's automotive industry is identified to have occurred in four phases. In the first (1947-1965) and second phase (1966-1979), the important policies identified were related to protection, indigenisation and regulation of the industry. On the one hand, these policies helped India to build an indigenous automotive industry, while on the other it led to unsatisfactory industry performance. In the third phase (1980-1990), the single most important policy identified was the one with regard to relaxation in the means of technology acquisition. The foreign competition inducted into the industry transformed its dynamics. Lastly, in the fourth phase (1991 onwards) the liberalisation with regard to foreign investment had a significant influence on the Indian automotive industry as we see it today. This work traces the evolution of the automotive industry from its inception to present day and identifies the important policies made by the Indian government. The work also studies the influence of important policies on the development of the industry. --India,Automotive,Industrial Policy,Government Policy,Government Influence