The monitoring of the power quality helps to lower the energy costs and to prolong the machine's life. Smart metering is such a complete end to end solution which minimizes the several errors and helps in distributing Quality Power. It is an energy policy for consumers to provide them a user friendly face in dealing with utility (especially the Electricity) bills. It provides the users, a Digital Meter which displays the real time power consumption every time in very friendly and detailed format and a website to analyze their consumption and expenses on energy, using different types of graphs, tabulated and manipulated data. It not only comforts their users but also give relief to the distribution company by minimizing power losses by using automatic Power factor maintenance technique, and providing antipower theft capability. It also gives a control of power distribution through which Distribution Company can limit the user from exceeding usage of power in specific time duration.
The complex conjugate ambiguity in Fourier domain optical coherence tomography (FDOCT) is a major roadblock that prevents full-detector resolution for the depth scan in single shot operation for the individual A-scan. Current techniques to eliminate this problem involve changing the experimental set-up, usually complicating the OCT system. In this work we show that the standard FDOCT spectrum data when resampled appropriately can be cast exactly in terms of type-1 discrete cosine transform (DCT). Additionally, a sparse reconstruction method in the DCT domain enables image recovery with full-detector resolution, thus effectively doubling the depth scan resolution. In a realistic simulation study we demonstrate full-detector resolution for a discrete reflective target by successfully resolving closely spaced reflective peaks that cannot be separated using the standard Fourier transform based reconstruction. Experimental results on reflective glass sheet targets further validate the methodology. The results of the proposed technique suggest that full resolution FDOCT systems may be implemented practically without additional hardware costs and system complexity.
<p class="Abstract"><span lang="EN-US">In the present era, teaching can occur either on a chalkboard or on a projected powerpoint presentation on the wall. Traditional teaching methods such as blackboards and powerpoint presentations are being phased out in favor of enriched learning experiences provided by emerging edtech. With the closure of schools due to COVID-19, the demand for online educational platforms has also increased. Furthermore, some of the recent trends in edtech include personalized learning, gamification and immersive learning with eXtended Reality (XR) technologies. Due to its immersive experience, XR is a pioneering technology in education, with multiple benefits including greater motivation, a positive attitude toward learning, concrete learning of abstract concepts, and so on. Existing Augmented Reality (AR) based education applications often rely on unimodal input such as marker-based trigger to launch the educational content. Hence, this work proposes a multi-modal interface to enable the content delivery through marker and speech recognition-based content delivery. Additionally, the proposed work is designed as mobile based AR platform with the regional language support to increase the ubiquitous accessibility of the AR content. Thus, the proposed mobile AR based enriched learning (AREL) platform provides a multi-modal mobile based educational AR platform for primary students. Based on the feedback received after the usage, it is observed that AREL improves the learning experience of the students.</span></p>
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