288 research outputs found
Sustainability in Internet of Things: Insights and scope
No full textThe study explores the sustainability in Internet of Things (IoT) by conducting bibliometric analysis of extant literature on the topic. For bibliometric analysis, we employ Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) method of systematic literature review. Year-wise, Author-wise, Citation-wise, Country-wise, Source-wise, Affiliation-wise and keywords-wise listing are the parameters to identify the trend and future scope of sustainability in IoT. We use Scopus database to list the extant literature. The study suggests that sustainability in IoT is explored in different fields like smart cities, agriculture, edge computing, block-chain, forecasting and energy utilization. This study provides insights on current trends and future scope of sustainable use of Io
Collective excitations in layered materials with momentum-resolved electron energy loss spectroscopy
Get PDFStrong Coulomb interactions are either suspected or known to play a prominent role in material classes such as high temperature superconductors, charge density waves, and Mott insulators among many others. These interactions are quantified by the charge density response function, chi(q,w) (or the closely related inverse dielectric function). The measurement of the energy- and momentum-resolved chi(q,w) over a large phase space of q and w, however, presents a significant experimental challenge. Traditional methods to measure chi(q,w) have suffered from either one or more major drawbacks. To address this problem, the development of a spectroscopic technique, momentum-resolved electron energy loss spectroscopy (M-EELS), was undertaken. Because many of the material classes that exhibit these unusual ground states tend to be layered or quasi-two dimensional, M-EELS presents a promising approach to measuring the energy- and momentum-resolved charge density response. Since the technique is not widely used, however, the M-EELS results obtained as part of this thesis were compared to other probes in the relevant ranges of phase space to ensure consistency. Furthermore, a theoretical framework was worked out to demonstrate explicitly the relationship between the scattering cross section and c(q,w). M-EELS experiments were conducted on a high-temperature superconductor, Bi2Sr2CaCu2O8+d, a charge density wave material, TiSe2, and a topological insulator, Bi2Se3. It was determined that the bosonic origin of quasiparticle kinks often seen in angle-resolved photoemission studies can be identified using M-EELS. Lastly, the observation of a novel electronic collective mode in TiSe2 is presented as strong evidence for an excitonic insulator phase in this compound.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2017-12-01The student, Anshul Kogar, accepted the attached license on 2015-08-28 at 14:26.The student, Anshul Kogar, submitted this Dissertation for approval on 2015-08-28 at 14:40.This Dissertation was approved for publication on 2015-09-08 at 14:22.DSpace SAF Submission Ingestion Package generated from Vireo submission #8678 on 2016-03-08 at 11:05:02Made available in DSpace on 2016-03-08T17:21:39Z (GMT). No. of bitstreams: 2
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Android game development with AppInventor
Get PDFThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 94).AppInventor is an educational learning tool provided by MIT that allows users to build Android apps without any knowledge of programming. As AppInventor gains popularity amongst educators and students around the world, it will become increasingly important to ensure that the tool offers its users the breadth and depth of app-development functionality they desire. In anticipation of AppInventor's expanding role and influence in educational institutions worldwide (middle schools and high-schools, primarily), this thesis focuses on the age group of 3rd to 12th grade students, and on the topic that is of greatest interest to them: gaming, animation, and graphics. The aim of this thesis is to identify AppInventor's existing capabilities and limitations with respect to game development, and to implement ideas (both pedagogical and technological in nature) that will improve the diversity, complexity, aesthetic appeal, and performance of games that can be built using AppInventor. The author of this thesis believes that if AppInventor's game development capabilities can be augmented, the adoption rate of the tool and its popularity amongst school students will be impacted very positively. In this thesis, the author describes his personal experiences teaching AppInventor game development in India and USA, as well as the limitations (in teaching methodology and in AppInventor's feature set) that he identified through this experience. The author's primary contributions are the development of a hands-on curriculum for a 40-hour AppInventor Game Development course, and the implementation of several new features and components for AppInventor. The author will be traveling to China and India in Summer 2012 to test to what extent his creative curriculum and novel AppInventor modifications facilitate the development of games using AppInventor.by Anshul Bhagi.M.Eng
Interactions between auditory and visual motion mechanisms and the role of attention: psychophysics and quantitative models
No full textThe human brain continuously receives sensory input from the dynamic physical world via various sensory modalities. In many cases, a single physical event generates simultaneous input to more than one modality. For example, a ball hitting the ground generates both visual and auditory input. The human brain has developed mechanisms to take advantage of the correlations between inputs to different modalities to form a uniform and stable percept. Recently, there has been a lot of research interest, psychophysical, neurophysiological and computational, to explore the mechanisms involved in crossmodal interactions in general and auditory-visual interactions in particular.
The current thesis makes three significant contributions to the field of auditory-visual interactions. First, I designed a comprehensive study to psychophysically examine the interactions between auditory and visual motion mechanisms for three different motion configurations: horizontal, vertical and motion-in-depth. I showed that simultaneous presentation of a strong motion signal in one modality influences perception of a weak motion signal in the other modality both when the weak motion in presented in the visual, as well as in the auditory modality. I further observed that crossmodal aftereffects were induced only when subjects adapted to spatial motion in the visual modality and not in the auditory modality. However, adaptation to auditory spectral motion did induce vertical visual motion aftereffects. To my knowledge, this is the first report of auditory-induced visual aftereffects. Second, I conducted psychophysical experiments to study the effects of spectral attention on the visual and the auditory motion mechanisms and showed that there are similar attentional effects on motion mechanisms within the two modalities. Third, I developed a neurophysiologically relevant computational model to provide a possible explanation for crossmodal interactions between the auditory and the visual motion mechanisms. In addition, I developed a model that can explain the observed experimental findings on the role of spectral attention in modulating motion aftereffects. The results obtained from both the model simulations agree very closely with the human behavioral data obtained from the experiments.Ph.D.Includes bibliographical references (p. 139-144)
Development of thermal displays for haptic interfaces
No full textThesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 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 97-102).This thesis studied the effect of different stimulus parameters on the thermal response of the skin. A set of thermal patterns, known as thermal icons, was presented to participants using a thermal display mounted on the hand. The thermal responses of the skin were studied to understand which features of the thermal stimuli were important and could be perceived by users. The effectiveness of these patterns was evaluated for applications involving hand-held and wearable thermal devices. In the first series of experiments, a set of six thermal icons was developed and presented on the thenar eminence and the fingertips. The second experiment was conducted on the wrist with a revised set of thermal icons which had a shorter duration and were presented relative to each participant's baseline skin temperature. The information transfer (IT) values for the thermal icons presented on the wrist-mounted thermal display demonstrated that the information processing capabilities of the thermal sensory system may rival those achieved with vibrotactile inputs. To date, thermal icon studies have only used the temporal properties of stimuli and not the spatial properties. A set of two experiments was conducted to examine how the spatial and temporal properties of thermal stimuli interact. The results showed that the temporal properties of thermal stimulation can influence the perceived location of a thermal stimulus. This space-time dependency for the thermal sensory system provides an extra dimension to use to present information in a thermal display and potentially could result in a display that functionally has a higher spatial resolution than the number of thermal elements would indicate.by Anshul Singhal.S.M
CRNet: Convolutive Recurrent Network for Suspect Face Identification
No full textIdentifying suspects in critical situations-particularly when they are wearing scarves, masks, or are in environments with light obstructions and concealed facial expressions-poses significant challenges. To address these issues, a method known as the Convolutive Recurrent Network (CRNet) for suspect face identification is proposed. CRNet utilises deep neural networks, specifically the Residual Network-50, leveraging a transfer learning approach for efficient feature extraction. In addition, Bidirectional Long Short-Term Memory (BiLSTM) layers are employed to capture spatial and recurrent features, with BiLSTM layers serving as the core component of the model. CRNet is designed to overcome the limitations of current models in managing complex situations, such as scarves, spectacles, high illumination, and varied expressions. CRNet fills this gap by integrating mechanisms that provide flexibility for ambiguous features and variable lighting conditions. Experimental and comparative analysis demonstrates that CRNet significantly outperforms existing methods, providing notable improvements in both accuracy and reliability. This approach introduces a rapid feature-learning method for precise suspect identification by integrating spatial dependencies, enhancing versatility across various computer vision domains. The model’s potential impact on criminal investigations is substantial due to its fast bidirectional feature processing. Experimental results demonstrate the robustness and adaptability of CRNet, achieving accuracy rates of 97.46% on the Extended Cohn-Kanade dataset, 98.08% on the Augmented Reality dataset, and 99.58% on the Extended Yale B dataset-substantially surpassing the baseline accuracy of 46.00%
Big data analytics in business process: Insights and implications
No full textThe study explores the role of big data analytics (BDA) in business process by presenting a comprehensive review of listed literature on Scopus database. 1122 studies are analyzed keyword-wise to identify the trend and future scope of BDA in business process. The study suggests that none of business process aspects are untouched by big data analytics. From planning to execution, manufacturing to customer satisfaction, costing to performance management and corporate governance to public relation, all aspect of business process has employed big data analytics tools in one way or other. BDA has also admitted its presence in different business domain like supply chain, service-industry, industry 4.0 and sustainable business practices. Internet of Things (IoT), Artificial Intelligence (AI), Deep learning, Decision support system, Neural network, Predictive analysis, Cloud computing and Machine learning are mostly used big data analytics tools. Keyword analysis also provide insights of currently researched topics and future scope for under researched topic
Detection of homophobia & transphobia in Malayalam and Tamil: Exploring deep learning methods
No full textThe increase in abusive content on online social media platforms is impacting the social life of online users. Use of offensive and hate speech has been making social media toxic. Homophobia and transphobia constitute offensive comments against LGBT + community. It becomes imperative to detect and handle these comments, to timely flag or issue a warning to users indulging in such behaviour. However, automated detection of such content is a challenging task, more so in Dravidian languages which are identified as low resource languages. Motivated by this, the paper attempts to explore applicability of different deep learning models for classification of the social media comments in Malayalam and Tamil languages as homophobic, transphobic and non-anti-LGBT + content. The popularly used deep learning models-Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) using GloVe embedding and transformer-based learning models (Multilingual BERT and IndicBERT) are applied to the classification problem. Results obtained show that IndicBERT outperforms the other implemented models, with obtained weighted average F1-score of 0.86 and 0.77 for Malayalam and Tamil, respectively. Therefore, the present work confirms higher performance of IndicBERT on the given task on selected Dravidian languages
Systemic review of AI reshaped blockchain application
No full textThe perceived benefits of integrating Blockchain Technology (BT) and Artificial Intelligence (AI) have profound importance from qualitative and quantitative perspectives to real-world applications. However, the academic literature connecting the two is largely absent. This study reviews the adoption of Blockchain applications in diversified sectors that contribute to its reshaping through AI and are not limited to digital currency, trade connection, or information processing. A term-searching mechanism addresses the research question through a formalized plan for reviewing field-wise blockchain papers. The keywords Blockchain, blockchain-based applications, and artificial intelligence assess the secondary literature published until the first quarter of 2021. Twenty-one use cases where BT proved its permanence theoretically or empirically about AI are identified based on equality and feasibility.
Moreover, the potential of BT goes beyond the hindrances of techno-socio-economic space concerns, thus contributing to research and business innovation. This study can be considered one of the first academic articles connecting BT and its application spread over IT and related industrial environments/sectors/ fields for improving the movement of AI. In addition to trust and awareness, integrating AI and Blockchain will lead to a future beyond the reactive machine's limitation
The finite element based study on active attenuation of structural vibration using porous ferroelectric ceramics
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