185 research outputs found
Brillouin nonlinearity characterizations of a high refractive index silicon oxynitride platform [Invited]
Full text linkSilicon oxynitride (SiON) is a low-loss and versatile material for linear and nonlinear photonics applications. Controlling the oxygen-to-nitrogen (O/N) ratio in SiON provides an effective way to engineer its optical and mechanical properties, making it a great platform for the investigation of on-chip optomechanical interactions, especially the stimulated Brillouin scattering (SBS). Here, we report the Brillouin nonlinearity characterization of a SiON platform with a specific O/N ratio (characterized by a refractive index of n = 1.65). First, we introduce this particular SiON platform with fabrication details. Subsequently, we discuss various techniques for the on-chip Brillouin nonlinearity characterizations. In particular, we focus on the intensity-modulated pump-probe lock-in amplifier technique, which enables ultra-sensitive characterization. Finally, we analyze the Brillouin nonlinearities of this SiON platform and compare them with other SiON platforms. This work underscores the potential of SiON for on-chip Brillouin-based applications. Moreover, it paves the way for Brillouin nonlinearity characterization across various material platforms
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
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
A Global Analysis on Microgrids through the PESTEL Framework
No full textMicrogrids enable distribution of electricity with higher shares of variable renewables, higher power quality, greater reliability and higher efficiency. There are a large number of factors in addition to the technology, which affect their shift towards market competitiveness and widespread adoption. The PESTEL framework, covering Political, Economic, Social, Technical, Environmental and Legislative factors, is used to identify and describe the drivers and barriers for microgrid development at the global level. The framework enables a broader approach to describe potential for microgrid applications. The results aim to provide engineers, project developers and microgrid specialists with an overview of the prospects for microgrid deployment.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Energy Technolog
How does trade impact agricultural productivity?
Full text linkThe student, Akshay Pandit, submitted this Thesis for approval on 2020-07-22 at 15:29.This Thesis was approved for publication on 2020-07-23 at 10:50.DSpace SAF Submission Ingestion Package generated from Vireo submission #15729 on 2020-10-02 at 15:34:07Made available in DSpace on 2020-10-07T22:44:48Z (GMT). No. of bitstreams: 2
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Previous issue date: 2020-07-23"Agricultural production has faced increased demands over the last half century from an expanding economy and population. We live in a globalized world, in which agriculture is deeply intertwined in international markets and trade. In this paper, we address the overarching research question: ""What is the impact of trade on agricultural productivity?''. To this end, we present a comprehensive statistical and econometric analysis on the relationship between international trade and agricultural production. We use national-scale data on crop yield, area harvested, production, and trade for the last half century (1961-2016) from the Food and Agricultural Organization of the United Nations. We introduce novel weighting and decomposition analyses to explore the relationship between trade and crop productivity. To determine the causal impact of trade on agriculture we implement instrumental variable (IV) econometric methods. We find that trade has led to an increase in global agricultural productivity over time (e.g. through increased productivity, the intensive margin). Global productivity gains have accrued primarily through the participation of more countries in global trade (e.g. expanding the area of contribution, the extensive margin). Additionally, we find that trade has enabled global crop consumption to increase. These findings indicate that trade openness leads to greater productivity in agriculture in general. This work highlights that trade can help to achieve productivity gains in agriculture and potentially help the world to address remaining yield gaps."Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-08-01The student, Akshay Pandit, accepted the attached license on 2020-07-22 at 15:28.Embargo set by: Seth Robbins for item 116267
Lift date: 2022-10-07T22:44:53Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemAuthor requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Onl
Behavioral data collection and simulation
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 45).On-demand ridesharing services, such as Uber and Lyft, and autonomous vehicles are significantly changing the landscape of transportation and mobility. In light of these disruptions, we aim to determine consumer preferences with regards to transportation and use this data to simulate and analyze the urban effects of smart mobility solutions. We collect behavioral data using Future Mobility Sensing (FMS), a smartphone and prompted-recall-based integrated activity-travel survey, and create simulations using the data with SimMobility, a simulation platform that integrates various mobility-sensitive behavioral models with state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services, and vehicular emissions. Enhancing these projects with on-demand preferences, individual patterns, and incentives as inputs, we aim to simulate and analyze a wide range of viable smart mobility solutions.by Akshay Padmanabha.M. Eng
A framework to search for machine learning pipelines
No full textThesis: M. Eng. in Computer Science, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (page 81).In this thesis, we present DeepMining, a framework to search for machine learning pipelines. The high-level goal of DeepMining is to solve the pipeline search problem: given a problem and a dataset, find the pipeline best-suited to solve that problem. The DeepMining platform serves as a testbed for developers to experiment with different methods of computing and evaluating machine learning pipelines. Specifically, developers have autonomy over how to evaluate different configurations in parallel, score a pipeline given a dataset and hyperparameter configuration, and efficiently search over the pipeline space. DeepMining was designed with modularity and extensibility in mind: developers can easily implement new search algorithms, scoring functions, and computation frameworks. At the same time, users can switch between these modules with minimal effort.by Akshay Ravikumar.M. Eng. in Computer Scienc
Structure vs. hardness through the obfuscation lens
No full textThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 71-[77]).Cryptography relies on the computational hardness of structured problems. While one-way functions, the most basic cryptographic object, does not seem to require much structure, as we advance up the ranks into public-key cryptography and beyond, we seem to require that certain structured problems are hard. For example, factoring, quadratic residuosity, discrete logarithms, and approximate shortest and closest vectors in lattices all have considerable algebraic structure. This structure, on the one hand, enables useful applications such as public-key and homomorphic encryption, but on the other, also puts their hardness in question. Their structure is exactly what puts them in low complexity classes such as SZK or NP [set-theoretic intersection symbol] coNP, and is in fact the reason behind (sub-exponential or quantum) algorithms for these problems. The question is whether such structure is inherent in different cryptographic primitives, deeming them inherently easier. We study the relationship between two structured complexity classes, statistical zero-knowledge (SZK) and NP [set-theoretic intersection symbol] coNP, and cryptography. To frame the question in a meaningful way, we rely on the language of black-box constructions and separations. Our results are the following: -- Cryptography vs. Structured Hardness: Our two main results show that there are no black-box constructions of hard problems in SZK or NP [set-theoretic intersection symbol] coNP starting from one of a wide variety of cryptographic primitives such as one-way and trapdoor functions, one-way and trapdoor permutations (in the case of SZK), public-key encryption, oblivious transfer, deniable encryption, functional encryption, and even indistinguishability obfuscation; -- Complexity-theoretic Implications: As a corollary of our result, we show a separation between SZK and NP[set-theoretic intersection symbol]coNP and the class PPAD that captures the complexity of computing Nash Equilibria; and -- Positive Results: We construct collision-resistant hashing from a strong form of SZK-hardness and indistinguishability obfuscation. It was previously known that indistinguishability obfuscation by itself does not imply collision-resistant hashing in a black-box way; we show that it does if one adds SZK-hardness as a "catalyst". Our black-box separations are derived using indistinguishability obfuscation as a "gateway", by first showing a (separation) result for indistinguishability obfuscation and then leveraging on the fact that indistinguishability obfuscation can be used to construct the above variety of cryptographic primitives and hard PPAD instances in a black-box manner.by Akshay Degwekar.S.M
Nutritional status of patients with liver disease
Full text linkIntroducere. Bolile hepatice determină frecvent tulburări nutriţionale, precum malnutriţia proteino-energetică, sar-copenia şi deficienţele de micronutrienţi, care agravează evoluţia clinică şi cresc riscul complicaţiilor. De aceea, evaluarea şi corectarea statusului nutriţional sunt esenţiale în managementul holistic. Scop. A revizui dovezile actuale privind statusul nutriţional în bolile hepatice cronice, cu accent pe prevalenţă, instrumente de evaluare şi relevanţa clinică a malnutriţiei şi sarcopeniei. Material şi metode. Această revizuire sintetizează date provenite din studii clinice, meta-analize şi ghiduri publicate între 2010 şi 2025 privind statusul nutriţional al pacienţilor cu boli hepatice. Sursele au fost selectate din bazele de date PubMed şi Scopus, pentru o analiză detaliată a metodelor de evaluare şi a definiţiilor sarcopeniei. Rezultate. Malnutriţia afectează 60-80% dintre pacienţii cu boală hepatică avansată, iar sar-copenia este raportată în 30-70%, în funcţie de stadiu şi eti-ologie. Cele mai utilizate metode de evaluare sunt Subjective Global Assessment, forţa de prindere şi analiza bioelectrică a impedanţei. Deficitul de vitamina D apare în peste 60% din cazuri, iar cel de zinc în 50%. Malnutriţia şi sarcopenia sunt asociate cu o creştere de 2-3 ori a complicaţiilor, spitalizări prelungite şi supravieţuire redusă, mai ales la pacienţii cu ciroză sau transplant. Evaluarea nutriţională precoce îmbunătăţeşte evoluţia şi reduce morbiditatea. Concluzii. Malnutriţia şi sarcopenia sunt complicaţii frecvente, dar adesea neglijate în bolile hepatice cronice. Evaluarea nutriţională de rutină trebuie integrată în îngrijirea hepatologică pentru a ghida intervenţiile ţintite, a îmbunătăţi rezultatele şi a menţine funcţia hepatică.Introduction. Liver disease often leads to nutritional disturbances such as protein-energy malnutrition, sarcopenia, and micronutrient deficiencies, which worsen clinical outcomes and increase complications. Therefore, assessing and improving nutritional status is essential in the holistic management of liver disease. Objective. To review current evidence on nutritional status in chronic liver disease, focusing on prevalence, assessment tools, and the clinical relevance of malnutrition and sarcopenia. Material and methods. This review summarizes data from clinical studies, meta-analyses, and guidelines published between 2010 and 2025 on the nutritional status of patients with liver disease. Sources were selected using PubMed and Scopus to make sure the analysis was detailed and thorough. Key focus areas included assessment methods and definitions of sarcopenia. Results. Malnutrition affects 60-80% of patients with advanced liver disease, with sarcopenia reported in 30-70%, depending on disease stage and etiology. Commonly used assessment tools include the Subjective Global Assessment, handgrip strength, and bioelectrical impedance. Vitamin D deficiency is present in over 60% of cases, and zinc deficiency in around 50%. Both malnutrition and sarcopenia are linked to a 2-3-fold increase in complications, prolonged hospitalization, and decreased survival, particularly among cirrhotic and transplant patients. Early and systematic nutritional assessment has been shown to improve outcomes and reduce morbidity. Conclusion. Malnutrition and sarcopenia are common yet often underrecognized complications in chronic liver disease. Routine nutritional assessment must be integrated into hepatology care to guide targeted interventions, enhance patient outcomes, and help out preserve liver function
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