10 research outputs found
Case of the Ahmedabad -Mumbai Corridor, India
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: M.C.P., Massachusetts Institute of Technology, Department of Urban Studies and Planning, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 126-131).The Thesis attempts to unpack how rural residents across India make decisions to migrate to urban centers in the Ahmedabad -Mumbai corridor and analyze if those decisions are concomitant with helping them achieve their migration intents. The study uses both qualitative and quantitative methods to analyze 52 origin -- destination pairs of migration. The qualitative methods, based on 52 field interviews, help understand the nuances of how and why people migrate. In analyzing patterns here, the study also heavily references the existing literature to establish departure points for quantitative studies. Basing itself on the model that these migration decisions are a trade-off between the wage differential and the social cost of being uprooted from one's native place, it attempts to quantify the gain and see if the gains proportionately increase with increased compromises on the migrant's social ties to their native place. It relies on geo-spatial analysis and several regression models to analyze the above mentioned phenomenon and offer a nuanced understanding of where value is captured/ lost in the process of migration. Finding that housing rents significantly offset wage differentials, a key part of understanding the value capture has been achieved through an analysis of housing rental data. The data analysis includes data collected via web-scraping and the gathering of about 25,000 datapoints, as well as rental and income data from 52 field interviews of migrants -- primarily working in the informal sector. In concluding its findings and analysis, the Thesis finds that solving information asymmetry , addressing integration of migrants into urban life whilst also maintaining their social ties with their native places, and state subsidies/ policies for cost effective and flexible rental housing may be the most critical pieces to improve the socio-economic mobility of migrants. The Thesis forms a basis for an entrepreneurial venture 'Bandhu'- by the author.by Rushil Palavajjhala.M.C.P.M.C.P. Massachusetts Institute of Technology, Department of Urban Studies and Plannin
Towards Normalization: De-Institutionalising Mental Healthcare and Catering the Youth
No full textThe thesis aims to investigate the role of the built environment with the help of design elements, in promoting mental health among young adults. The study will focus on how architectural design choices can contribute to- wards normalization and de-institutionalisation of mental healthcare facilities for the youth. The academic paper aims to explore the impact of the built environment on mental health in an ever changing modern society. The research will examine several literature studies, academic writings, case studies and primary sources through fieldwork, observations and interviews. Social and environmental factors which might influence the youth and how design choices can address these factors will also be analyzed. Overall, the thesis seeks to provide insights on how architects can create spaces that not only prevent mental illnesses but also promote mental wellbeing among young adults. While mental health is a very prominent topic currently, this thesis aims at providing an alternative approach towards the topic through architecture.Architecture, Urbanism and Building Sciences | Dwellin
Multi-Modal End-to-End Learning for Real-Time Monitoring of Sustainable Energy Systems
No full textThe growth of renewable energy technologies is leading to energy systems that are more reliant than ever on renewables such as Wind and Photovoltaic (PV) power. Despite their benefits in terms of sustainability, their ubiquity poses challenges in maintaining grid stability given their intermittency, emphasising the prediction of power fluctuations. Physical models and statistical approaches, especially for nowcasting (forecasting for 0-6 hours in the future), have been superseded by Machine Learning (ML) methods in terms of forecast accuracy (below 3% Root Mean Squared Error (RMSE)). Within ML, Artificial Neural Network (ANN) methods seem to perform particularly well for nowcasting. This project focuses on predicting solar and wind meteorology with that level of accuracy, and on how to best use the prediction to minimize the cost of maintaining a balanced energy system, i.e. one where power consumption matches production at any moment. Producing accurate power predictions based on Multi-Modal (MM) data and the extent to which prediction accuracy reduces system cost are challenges to be addressed in this thesis. MM and End-to-End (E2E) training (with the system cost as the task of an ANN based algorithm) are investigated to this end. MM learning involves handling information from multiple types of input (audio and visual, for example) for performing a ML task such as regression or classification. It is of interest for this project because it has been shown to outperform other NN approaches in predicting sudden changes in solar irradiance. E2E learning entails an algorithm design which predicts the end goal of a ML process directly from the inputs. This is pursued because it addresses the true task (cost minimization) of system operators as the focus of the ML algorithm. The proposed method consists of a NN architecture that learns to fuse features from MM data (sky imagery and meteorological sensor data) at intermediate layers of the network in order to predict PV or Wind generation. This prediction is then used as an input to an Optimal Power Flow (OPF) problem (which seeks to minimize generation costs in a power system, considering power balance and transmission network constraints to ensure the twin goals of economic and secure system operation). The proposed model is trained E2E, therefore it is informed by the minimized cost solved by the optimization, rather than the intermediate power prediction (as conventional approaches would involve). In an IEEE 6-bus system with PV generation, a sequential training baseline results in costs 10% higher than a perfect forecast, while our proposed MM4-E2E approach achieves costs only 7% higher, a significant improvement. The intermediate prediction of PV power by MM4-E2E is also improved, with 18% lower RMSE by the proposed model compared to the baseline, explained by the enhancement of one modality by the other through MM learning. In a power system with two renewable sources, costs are reduced through the proposed model compared to a conventional approach (4% excess cost compared to 7%, measured against a perfect forecast), but power prediction accuracy is worse, sue to convergence to local minima.Electrical Engineering | Sustainable Energy Technolog
Sustainability and Paradigms of Mughal Architecture in Old Delhi 1526 – 1707
No full textThe thesis seeks to analyze the Mughal Architecture in Old Delhi from 1526 till 1707. Starting with a brief description about the history of India, it allows the reader to acknowledge the motifs of the Mughal sultanate and how it was established. The writing advances to introduce the six main Mughal emperors in Delhi such as Babur, Humayun, Akbar, Jahangir, Shah Jahan and Aurangzeb respectively. The six emperors not only passed on their architectural knowledge, but also built famous palaces in parts of Delhi that we, in today’s day call Old Delhi. Some of these palaces will be analyzed from a sustainable standpoint to understand how the architecture tackled the extreme climate conditions of the region. Through this categorical breakdown of the Mughal Architecture, the thesis aims towards understand and inspiring the use of vernacular architectural sustainability and its positive effects created by its utilization.AR2A011Architecture, Urbanism and Building Science
An energy-efficient BJT-based Temperature-digital converter based on a Continuous-Time Readout
No full textThis thesis presents the most energy-efficient BJT-based CMOS temperature-to-digital converter (TDC) reported to date. It is based on a continuous-time front-end, which avoids the kT/C noise limit incurred by previous discrete-time (switched-capacitor) front-ends. An energy-efficient capacitively-coupled instrumentation amplifier (CCIA) boosts ΔVbe, a small temperature-dependent voltage (~150uV/°C) derived from two BJTs, before it is digitized by an ADC. Like conventional switched-capacitor front-ends, the CCIA’s gain is determined by capacitor ratios, resulting in similar levels of accuracy. Fabricated in a 0.18um CMOS process, the TDC achieves an inaccuracy of ±0.12°C (3σ) from -40°C to +125°C after a room-temperature calibration. This is comparable with the state-of-the-art. Furthermore, it achieves 1.27mK resolution in a 320 millisecond conversion time, while consuming 8.9uW. This corresponds to a state-of-the-art resolution FoM of 4.5pJ∙K2.Electrical Engineerin
Artificial Intelligence and Ownership Issues – A Comparative Analysis
No full textThis research delves into the evolving intersection of Artificial Intelligence (AI) and Intellectual Property (IP) law, with a particular focus on the copyrightability of AI-generated works. As AI technologies increasingly demonstrate the capacity to autonomously create literary, artistic, and musical content, they challenge the foundational principles of copyright law, which traditionally hinge on human authorship and creativity. The study critically examines whether existing legal frameworks, especially in India, are equipped to address the complexities introduced by AI-generated content and whether such works can be granted copyright protection under current statutes. The paper begins by contextualizing AI as a transformative force in the 21st century, influencing sectors from healthcare to creative industries. It highlights the growing reliance on AI in generating original content and the subsequent legal ambiguity surrounding authorship and ownership. The Indian Copyright Act, 1957, while recognizing computer-generated works, lacks clarity on the status of AI-generated content, particularly in the absence of human intervention. This legal gap raises fundamental questions about the definition of creativity, authorship, and the scope of protection under copyright law. Through comparative legal analysis, the study explores how jurisdictions such as the United States, United Kingdom, and European Union interpret and apply copyright principles to AI-generated works. Landmark cases and statutory provisions are examined to understand the global legal stance on authorship, originality, and ownership in the context of AI. The research also evaluates the relevance of international treaties like the Berne Convention and TRIPS Agreement, noting their silence on AI while identifying interpretive possibilities that could accommodate AI-generated works. Key research questions include whether AI-generated works meet the threshold of creativity required for copyright protection, who qualifies as the author or owner, and whether AI can be granted legal personhood or co-authorship status. The study also considers the implications of denying copyright protection to AI-generated works, including potential violations of competition law and disincentives for innovation and investment in AI technologies. Employing a doctrinal methodology, the research relies on secondary sources such as legal commentaries, international reports, and judicial decisions. It aims to propose viable legal reforms that align Indian copyright law with international standards, ensuring that AI-generated works receive appropriate recognition and protection. Ultimately, the study advocates for a nuanced and adaptive legal framework that reflects the realities of technological advancement. It emphasizes the need for legislative clarity, judicial interpretation, and international cooperation to resolve current ambiguities and ensure that the legal system evolves in tandem with AI’s growing role in creative processes. The research concludes by offering practical suggestions for harmonizing Indian copyright law with global best practices, thereby safeguarding both innovation and the integrity of intellectual property rights in the age of artificial intelligence
Low complexity differential geometric computations with applications to human activity analysis
No full textabstract: In this thesis, we consider the problem of fast and efficient indexing techniques for time sequences which evolve on manifold-valued spaces. Using manifolds is a convenient way to work with complex features that often do not live in Euclidean spaces. However, computing standard notions of geodesic distance, mean etc. can get very involved due to the underlying non-linearity associated with the space. As a result a complex task such as manifold sequence matching would require very large number of computations making it hard to use in practice. We believe that one can device smart approximation algorithms for several classes of such problems which take into account the geometry of the manifold and maintain the favorable properties of the exact approach. This problem has several applications in areas of human activity discovery and recognition, where several features and representations are naturally studied in a non-Euclidean setting. We propose a novel solution to the problem of indexing manifold-valued sequences by proposing an intrinsic approach to map sequences to a symbolic representation. This is shown to enable the deployment of fast and accurate algorithms for activity recognition, motif discovery, and anomaly detection. Toward this end, we present generalizations of key concepts of piece-wise aggregation and symbolic approximation for the case of non-Euclidean manifolds. Experiments show that one can replace expensive geodesic computations with much faster symbolic computations with little loss of accuracy in activity recognition and discovery applications. The proposed methods are ideally suited for real-time systems and resource constrained scenarios.Dissertation/ThesisM.S. Electrical Engineering 201
Statistical and Dynamical Modeling of Riemannian Trajectories with Application to Human Movement Analysis
No full textabstract: The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
Diversity Promoting Online Sampling for Streaming Video Summarization
No full textabstract: Video summarization is gaining popularity in the technological culture, where positioning the mouse pointer on top of a video results in a quick overview of what the video is about. The algorithm usually selects frames in a time sequence through systematic sampling. Invariably, there are other applications like video surveillance, web-based video surfing and video archival applications which can benefit from efficient and concise video summaries. In this project, we explored several clustering algorithms and how these can be combined and deconstructed to make summarization algorithm more efficient and relevant. We focused on two metrics to summarize: reducing error and redundancy in the summary. To reduce the error online k-means clustering algorithm was used; to reduce redundancy we applied two different methods: volume of convex hulls and the true diversity measure that is usually used in biological disciplines. The algorithm was efficient and computationally cost effective due to its online nature. The diversity maximization (or redundancy reduction) using technique of volume of convex hulls showed better results compared to other conventional methods on 50 different videos. For the true diversity measure, there has not been much work done on the nature of the measure in the context of video summarization. When we applied it, the algorithm stalled due to the true diversity saturating because of the inherent initialization present in the algorithm. We explored the nature of this measure to gain better understanding on how it can help to make summarization more intuitive and give the user a handle to customize the summary
Side-Chain Free Semiconducting Polymer for High-Performance n‑Type Organic Electrochemical Transistors
No full textThe development of organic electrochemical transistors (OECTs) critically depends on the design and characterization of mixed-conducting, high-performance conjugated polymers (CPs) as channel materials, particularly for n-type OECTs. In this study, we present a novel strategy to enhance the OECT performance of a semiconducting polymer film via a postdeposition ester pyrolysis of thermally cleavable side chains, thus facilitating ion incorporation and transport within the bulk. Our approach relies on the synthesis of a high glass-transition, rigid-rod polymer, able to withstand the pyrolysis temperature without deformation and maintain the voids formed from the pyrolysis reaction which removes the thermally cleavable ester side chains. After side-chain cleavage, the resulting film exhibits increased porosity, hydrophilicity, and crystallinity. By creating bulk porosity in thin films via this approach, ion diffusion is enhanced, resulting in a superior μC* figure of merit up to 158.85 F cm–1 V–1 s–1, and a corresponding increase in normalized transconductance (31.67 S cm–1). In addition, the device switching speed and long-term stability are also observed to increase, further demonstrating the benefit of nanoscale porosity for mixed conductivity semiconductors
