Abstract

Remote Triggered Labs (RTL) are helpful for students to work on laboratory experiments virtually anytime, anywhere. Such setups can facilitate distance learning and are helpful during pandemics. In this paper, the use of Computer Vision (CV) is demonstrated for RTL experiments. For this, a use-case of the Conservation of Mechanical Energy experiment is considered. A CV-based approach is used to estimate an object’s velocity whose setup primarily consists of a microprocessor, a camera and infrared (IR) sensors. The experiment is recorded, and various CV techniques are employed to estimate the object’s velocity. This paper also compares a CV-based and an IR sensor-based approach to estimate the object’s velocity. Linear regression applied to the CV-based implementation resulted in an optimal mean-squared error (MSE), nearly 10 times better than IR-based implementation.

Abstract

An engineer in a product company is expected to design a good solution to a computing problem (Design skill) and articulate the solution well (Expression skill). We expect an industry-ready student (final year student or a fresh campus hire) as well to demonstrate both these skills when working on simple problems assigned to them. This paper reports on the results when we tested a cohort of participants (N=16) for these two skills. We created two participant groups from two different tiers of college, one from a Tier 1 college (who were taking an advanced elective course), and another from Tier 2 colleges (who had been hired for internship in a SaaS product company). We gave them a simple design problem and evaluated the quality of their design and expression. Design quality was evaluated along three design principles of Abstraction, Decomposition, and Precision (adapted from the Software Engineering Book of Knowledge). Expression quality was evaluated using criteria we developed for our study that is based on the diversity and density of the expressions used in the articulation. We found the students lacking in design and expression skills. Specifically, a) they struggled with abstraction as a design principle, b) they did not use enough modes of expressions to articulate their design, and c) they did not use enough formal notations (UML, equations, relations, etc.). We also found significant difference in the performance between the two participant groups.

Abstract

Open source software is increasingly being used by various organi- zations. Open source licenses such as GPL, MIT, Apache, Mozilla, BSD, etc have varying terms and conditions for usage, modification, and distribution. Given the availability of source code for open source software, it is difficult to scrutinize adherence of licenses and more often than not it is left to the good faith of organizations and individuals. Violation of licensing terms knowingly or unknow- ingly can lead to copyright, ethical, compliance and security issues. In this paper, we propose a blockchain based approach in managing open source software licenses by enforcing certain practices to improve awareness and increase transparency, thus minimizing the possibility of violations. We present a solution design that relies on the use of 4 modules: InterPlanetary File System (IPFS), Smart contracts, Transaction manager (Meta-Mask) and a Permissioned Blockchain to enforce the conformance of licenses

Abstract

Research in hardware and software support for Virtual Reality (VR) has significantly increased over the last decade. Given the software platform fragmentation and hardware volatility, there is an apparent disconnect among practitioners while building applications in the VR domain. This paper proposes a role-based model template as a meta-model to specify the bare minimum VR software system. We conducted a grounded-theory-based qualitative study on prevailing and phased-out VR SDKs and standards to propose this meta-model. This model template can help VR practitioners build open-source tools to develop, design, and test VR software systems.

Abstract

Material science enables us to develop a wide array of materials capable enough to address worldly problems.Velostat is a carbon-impregnated polythene material used to address many problems including gait-related problems. This paper presents a novel approach to analyze a gait-cycle parameter called ‘Stance-phase.’ We ideated, designed, and implemented a low-cost customizable Velostat based sensing mat to calculate the stance phase. We validated our VelGmat using sensor density, sensitivity, process time, and hardware performance metrics. Our validation results helped us improve the effectiveness of gait analysis. We conducted a comparative study between our VelGmat and commercially available mat to understand the contrast in stance phase values. We observed 95% accuracy in stance phase values compared with commercially available mat. Index Terms—Piezoresistive; Pressure sensing; Scan time; Sensor density; Gait cycle; Velostat

Abstract

A method for generating a limitless path in a virtual reality environment ( VR ) for a continuous locomotion within a real physical space using Head - Mounted - Display ( HMD ) device associated with a user is provided . The method includes determining a line segment between two points that corre sponds to an initial path travelled by the user . The method includes detecting a boundary of the VR environment to generate a next line segment . The method includes gener ating and adding a new line segment to end of the initial path . The method includes generating and adding the new line segment to the end of the next line segment . The method includes generating an updated path by adding the new line segment in a direction at the angle of shift angle to the direction of the next line segment .The method includes , configuring to output updated path as two - dimensional points to render updated path into VR environment .

Abstract

Image processing algorithms with intrinsic robust- ness to errors can be approximated for significant resource and energy savings while still meeting the end-user requirements. FPGA-based implementations can increase their suitability for real-time high-speed multimedia applications by leveraging Ap- proximate Computing, an independent field that explores meth- ods to reduce computation costs by allowing minor degradation in intermediate computations. With high volume of pixel level computations, corner detection algorithms such as the Harris Corner Detector (HCD), offer wide range of targets for such strategies. In this paper, we propose an hardware implementation of HCD that relies on approximating the intermediate multi- plication operations using Dynamic Range Unbiased Multiplier (DRUM). With run-time configurable bit-width control of DRUM instances, the visual quality of outputs is shown to depend on the varying accuracy of the corner response. We explore how the errors due to approximate operations propagate to the corner response and attempt to find a threshold that adapts to this inaccuracy across images. The experimental results for implementations on Virtex-7 and Zynq-7000 FPGA devices show that our approximate architecture can match the performance of other HCD implementations while hardly utilizing any on-board memory and signal processing resources. Synthesis results show that the proposed implementation achieves over 60% increase in maximum frequency compared to the base implementation. Finally, the quality metric analysis facilitates the selection of approximate configuration suited to the needs of an application. Index Terms—FPGA, Approximate Computing, DRUM, Image Processing, Harris Corner Detector, Bluespec, Zynq, Virtex

Abstract

The impact of climate change on the oxygen saturation content of the world’s surface waters is a signifcant topic for future water quality in a warming environment. While increasing river water temperatures (RWTs) with climate change signals have been the subject of several recent research, how climate change afects Dissolved Oxygen (DO) saturation levels have not been intensively studied. This study examined the direct efect of rising RWTs on saturated DO concentrations. For this, a hybrid deep learning model using Long Short-Term Memory integrated with k-nearest neighbor bootstrap resampling algorithm is developed for RWT prediction addressing sparse spatiotemporal RWT data for seven major polluted river catchments of India at a monthly scale. The summer RWT increase for Tunga-Bhadra, Sabarmati, Musi, Ganga, and Narmada basins are predicted as 3.1, 3.8, 5.8, 7.3, 7.8 °C, respectively, for 2071–2100 with ensemble of NASA Earth Exchange Global Daily Downscaled Projections of air temperature with Representative Concentration Pathway 8.5 scenario. The RWT increases up to7 °C for summer, reaching close to 35 °C, and decreases DO saturation capacity by 2–12% for 2071–2100. Overall, for every 1 °C RWT increase, there will be about 2.3% decrease in DO saturation level concentrations over Indian catchments under climate signals.

Abstract

Climate change exposes more frequent natural hazards and physical vulnerabilities to the built and natural environments. Extreme precipitation and temperature events will have a significant impact on both the natural environment and human society. However, it is unclear whether precipitation and temperature extremes increase physical vulnerabilities across scales and their links with large-scale climate indices. This study investigates the relationship between precipitation and temperature extremes, as recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), and large scale climatological phenomenon indices (Indian Summer Monsoon Index (ISMI), Arctic Oscillation (AO), and North Atlantic Oscillation (NAO)), using India as a case study. Our Bndings show that extreme warm indices were primarily negatively related to ISMI and positively related to extreme cold indices. According to Pearson’s correlation coefBcients and Wavelet Transform Coherence (WTC), extreme warm indices were negatively related to ISMI and positively related to extreme cold indices. The extreme precipitation indices had a significant positive relationship, primarily with AO. Furthermore, from 1951 to 2018, India experienced an increase in warm extremes over western, central, and peninsular India, while cold indices increased over northwest India. Precipitation extremes of more than one day, more than Bve days, very wet and extremely wet days have increased across India except in the Indo-Gangetic plains, while heavy and very heavy precipitation days, consecutive wet days, and consecutive dry days have decreased.

Abstract

This paper proposes a novel methodology of analyzing mobile Internet of Things (IoT) data by performing spatial and anthropogenic factor-based thematic interactions with it to retrieve interesting patterns that account for the data variation. In order to test out this methodology, a study is conducted by collecting Particulate Matter (PM) data across India using a mobile IoT node, and look into the neighbouring spatial and anthropogenic factors such as human activities, settlement patterns and vegetation profile corresponding to each geo-location of the PM data. By performing the spatial factor analysis on the mobile IoT data, we evaluated the influence of human activities on PM10 levels, most significantly observed for 0