1,733,617 research outputs found
Architecture validation of VFP control for the WiNC2R platform
No full textA Cognitive Radio processing requires intelligent transceiver which can be easily programmed and reconfigured dynamically to support multiple protocols. The Winlab Network Centric Cognitive Radio (WiNC2R) platform is based on the concept of Virtual Flow Pipelining Paradigm. WiNC2R can support per packet protocol adaption through the reconfiguration of function sequencing. Since WiNC2R platform can be programmed by adding additional functions in software, and flow sequencing reprogramming architecturally supported in hardware, it can easily support future protocols. The latest version of WiNC2R has advanced shared VFP control unit, cluster based SoC architecture with all the processing engines in an 802.11a like OFDM transmitter flow. It is very important to characterize the VFP overhead with the realistic protocol processing examples to understand the performance and cost penalties of added flexibility, and establish the base for the comparison with Software Defined Radio approach. The performance analysis of the VFP will give detailed insight about the various latencies involved in the VFP processing. VFP Architecture is validated to see that the current implementation does meet the requirements of the WiNC2R platform. This performance analysis will help in characterizing VFP overhead under varying throughput requirements. Architectural validation of VFP will characterize certain parameters of the system programming, like reschedule period, guard time, etc.M.S.Includes bibliographical referencesby Akshay Jo
Content-based image retrieval of digitized histopathology via boosted spectral embedding (BoSE)
No full textContent-based image retrieval (CBIR) systems allow for retrieval of images from a database that are similar in visual content to a query image. This is particularly useful in scenarios such as digital pathology, where text-based descriptors alone might be inadequate to accurately describe image content. By representing images via a set of quantitative image descriptors, the similarity between a query image with respect to archived, annotated images in a database can be computed and the most similar images retrieved. Recently, non-linear dimensionality reduction (NLDR) methods have become popular for embedding high dimensional data into a reduced dimensional space while preserving local object adjacencies, thereby allowing for object similarity to be determined more accurately in the reduced dimensional space. However, most dimensionality reduction (DR) methods implicitly assume, in computing the reduced dimensional representation, that all features are equally important. Erroneous or noisy features could potentially result in dissimilar images being mapped close to each other in the reduced embedding space. In this work we present Boosted Spectral Embedding (BoSE), a variant of the traditional Spectral Embedding (SE) NLDR method, which unlike SE utilizes a boosted distance metric (BDM) to selectively weight individual features to subsequently map the data into a reduced dimensional space. In this work BoSE is evaluated against SE (which employs equal feature weighting) in the context of CBIR of digitized prostate and breast cancer histopathology images. Across 154 hematoxylin and eosin (H&E) stained histopathology images corresponding to benign and malignant prostate cancer biopsy images, low and high grade ER+ breast cancer studies, and HER2+ breast cancer H&E images, BoSE outperformed SE both in terms of CBIR-based (area under the precision recall curve) and classifier-based (classification accuracy) performance measures. Consistent trends were observed when embedding the data into spaces with different dimensions. Our results suggest that BoSE could serve as an important tool for CBIR and classification of high dimensional biomedical data.M.S.Includes bibliographical referencesIncludes vitaby Akshay Sridha
Echoes of Home: Akshay\u27s Journey from Mumbai to Toronto
Full text linkAkshay moved from Mumbai to Toronto in search for a life of his own. He had thought of all the challenges that would come up, but the one challenge he had not thought of had happened
Akshay Sharma (3MT 2021 Competition)
No full textEntry in the 2021 3MT (Three Minute Thesis) Competition. Title: Mathematics, a sword for fighting cancer
An object oriented approach to matrix analysis of structures
Full text linkThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2002.Includes bibliographical references (p. 52).by Akshay R. Sthapit.S.M
Weighted K-nearest neighbor algorithm as an object localization technique using passive RFID tags
No full textTechnologies using identification by radio frequencies (RFID) are experiencing rapid development and healthcare is a major application area benefiting from it. Highly pervasive RFID enables remote identification, tracking and localization of the medical staff, patients, medications and equipment, thus increasing safety, optimizing in real-time management and providing support for new ambient-intelligent services. This thesis describes and evaluates an algorithm that enables object localization and tracking using passive RFID tags. This thesis also describes scenarios of how this technology can be used as a part of building a smart trauma resuscitation room by tracking the equipments. The main contribution of this thesis is the adaptation of the Weighted K-Nearest Neighbor Algorithm as a localization technique to track objects in a confined and crowded space by using passive RFID tags. The input parameter to the algorithm is the received signal strength indicator (RSSI), which gives a measure of back-scattered radio frequencies from passive tags. While using RFID technology special attention has to be given to the placement of antennas to get the optimum result. Therefore, we analyzed various antenna placement configurations with mean error and error consistency as the two performance parameters. The detection of multiple tags and human occlusion are two major concerns while tracking tags in a confined space with many team members collaborating on solving a problem. The RF signal can be interrupted by people walking around randomly and holding multiple (tagged) instruments at the same time. While the algorithm worked fine when tracking multiple tags, we had to modify the experimental set-up and attach an antenna onto the ceiling (which we call a vertical antenna), so that even if all the wall antennas are blocked we get at least one input parameter to base our localization decision on. We evaluated the algorithm for different combinations of configurations and number of neighbors, and achieved the following results. The best results were obtained for the 3 antennae (placed orthogonally) configuration considering the 4 nearest neighbors wherein a mean error rate of 15% of the maximum possible error was achieved under ideal conditions. We tested the algorithm for different human occlusion scenarios i.e. blocking 1 or 2 wall antennas, standing in random positions and then roaming in the field area randomly. The mean error rate for the standing scenario was measured as 20% of the maximum possible error and 18% in the case of roaming configuration. The error was found to be consistently within our defined maximum error for 100% of the recorded readings. The results obtained were found to be satisfactory for our application where, more than the exact location of the object, knowing whether the object is within a particular region is good enough for the users to know what task is being carried out in the trauma bay. Also the algorithm holds good in an indoor environment having a lot of factors and materials which affect the RF signal disrupting accurate calculation of the location co-ordinates. The algorithm does not require extensive data collection prior to implementation which makes it easily deployable in any environment. Apart from the problems mentioned there are some other factors like materials on which the tags are attached and orientation of tags which were found to be potential hindrances for accurate localization. Acceptable solutions to these problems form a part of our future work.M.S.Includes bibliographical referencesby Akshay Shett
WaterDock-2.0
No full text<p>PyMol plugin for the Bridging water prediction algorithm WaterDock. </p>
Business Intelligence Computational Intelligence in Vehicle and Transportation System
No full textThe Traffic and Transportation system is big problem in the world. So business intelligence in vehicle and transportation system solve this problem and solution with the help of new technologies. In the computational intelligence in vehicle and transportation system used computer electrical and electronic conversion technology management. Akshay Shrikant Nehre "Business Intelligence (Computational Intelligence in Vehicle and Transportation System)" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30226.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30226/business-intelligence-computational-intelligence-in-vehicle-and-transportation-system/akshay-shrikant-nehr
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