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Electrical Engineering and Computer Sciences - Master's degree

Mostrando recursos 1 - 20 de 6.283

  1. Emotion recognition using wireless signals

    Zhao, Mingmin, S.M. Massachusetts Institute of Technology
    This thesis demonstrates a new technology that can infer a person's emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a person's body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition...

  2. Private sequential search and optimization

    Xu, Zhi, S.M. Massachusetts Institute of Technology
    We propose and analyze two models to study an intrinsic trade-off between privacy and query complexity in online settings: 1. Our first private optimization model involves an agent aiming to minimize an objective function expressed as a weighted sum of finitely many convex cost functions, where the weights capture the importance the agent assigns to each cost function. The agent possesses as her private information the weights, but does not know the cost functions, and must obtain information on them by sequentially querying an external data provider. The objective of the agent is to obtain an accurate estimate of the...

  3. On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment with rebalancing

    Wallar, Alexander James
    On-demand ride-sharing systems with autonomous vehicles have the potential to enhance the efficiency and reliability of urban mobility. However, existing ride-sharing algorithms are unable to accommodate high capacity vehicles and do not incorporate future predicted demand. This thesis presents a real-time method for high-capacity ride-sharing that scales to a large number of passengers and trips, dynamically generates optimal routes with respect to online demand and vehicle locations, and incorporates predictions of anticipated requests to improve the performance of a network of taxis. We experimentally assess the trade off between fleet size, capacity, waiting time, travel delay, and amount of predictions...

  4. Faster algorithms for matrix scaling and balancing via convex optimization

    Tsipras, Dimitrios
    In this thesis, we study matrix scaling and balancing, which are fundamental problems in scientific computing, with a long line of work on them that dates back to the 1960s. We provide algorithms for both these problems that, ignoring logarithmic factors involving the dimension of the input matrix and the size of its entries, both run in time Õ(m log K log² (1/[epsilon])) where e is the amount of error we are willing to tolerate. Here, K represents the ratio between the largest and the smallest entries of the optimal scalings. This implies that our algorithms run in nearly-linear time...

  5. Machine-learning models for predicting drug approvals and clinical-phase transitions

    Siah, Kien Wei
    We apply machine-learning techniques to predict drug approvals and phase transitions using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. Imputation methods are used to deal with missing data, allowing us to fully exploit the entire dataset, the largest of its kind. We achieve predictive measures of 0.74, 0.78, and 0.81 AUC for predicting transitions from phase 2 to phase 3, phase 2 to approval, and phase 3 to approval, respectively. Using five-year rolling windows, we document an increasing trend in the predictive power of these models,...

  6. Towards generative compression

    Santurkar, Shibani (Shibani Vinay)
    Graceful degradation is a metric of system functionality which guarantees that performance declines gradually as resource constraints increase or components fail. In the context of data compression, this translates to providing users with intelligible data, even in the presence of bandwidth bottlenecks and noisy channels. Traditional image and video compression algorithms rely on hand-crafted encoder/decoder pairs (codecs) that lack adaptability and are agnostic to the data being compressed and as a result, they do not degrade gracefully. Further, these traditional techniques have been customized for bitmap images and cannot be easily extended to the variety of new media formats such...

  7. Network oblivious transfer

    Raghuraman, Srinivasan
    Motivated by the goal of improving the concrete efficiency of secure multiparty computation (MPC), we study the possibility of implementing an infrastructure for MPC. We propose an infrastructure based on oblivious transfer (OT), which would consist of OT channels between some pairs of parties in the network. We devise information-theoretically secure protocols that allow additional pairs of parties to establish secure OT correlations using the help of other parties in the network in the presence of a dishonest majority. Our main technical contribution is an upper bound that matches a lower bound of Harnik, Ishai, and Kushilevitz (Crypto 2007), who...

  8. Fresnel-focusing and bessel-beam integrated optical phased arrays for optical trapping applications

    Notaros, Jelena
    Optical trapping and tweezing - the manipulation of particles using optical forces - enables direct interaction with biological samples and non-invasive monitoring of their properties. As such, optical trapping has become a common tool in biology with applications ranging from better understanding of DNA mechanics to non-invasive manipulation of red blood cells in vivo. While optical trapping using bulk optics is a well established technique, recent work has turned towards chip-based optical trapping using integrated devices. However, many of these integrated systems are fundamentally limited to passive demonstrations within microns of the chip surface. Integrated optical phased arrays, which manipulate...

  9. Making discrete decisions based on continuous values

    Sherman, Benjamin (Benjamin Marc)
    Many safety-critical software systems are cyber-physical systems that compute with continuous values; confirming their safety requires guaranteeing the accuracy of their computations. It is impossible for these systems to compute (total and deterministic) discrete computations (e.g., decisions) based on connected input spaces such as R. We propose a programming language based on constructive topology, whose types are spaces and programs are executable continuous maps, that facilitates making formal guarantees of accuracy of computed results. We demonstrate that discrete decisions can be made based on continuous values by permitting nondeterminism. This thesis describes variants of the programming language allowing nondeterminism and/or...

  10. Large-area CVD growth of two-dimensional transition metal dichalcogenides and monolayer MoS₂ and WS₂ metal-oxide-semiconductor field-effect transistors

    Shen, Pin-Chun
    Two-dimensional semiconducting materials such as MoS₂ and WS₂ have been attractive for use in ultra-scaled electronic and optoelectronic devices because of their atomically-thin thickness, direct band gap, and lack of dangling bonds. Methods for large-area growth of 2D semiconducting materials are needed to bring them to practical applications. This thesis aims to develop reliable methods for growing high-quality monolayer MoS₂ and WS₂ by CVD and explore their intrinsic electrical transport properties for electronic and optoelectronic device applications. The as-grown monolayer MoS₂ and WS₂ exhibit n-type semiconducting behavior with excellent optical properties. Various techniques are employed to characterize the CVD-grown materials,...

  11. Optimizing throughput architectures for speculative parallelism

    Abeydeera, Maleen Hasanka (Weeraratna Patabendige Maleen Hasanka)
    Throughput-oriented architectures, like GPUs, use a large number of simple cores and rely on application-level parallelism, using multithreading to keep the cores busy. These architectures work well when parallelism is plentiful but work poorly when its not. Therefore, it is important to combine these techniques with other hardware support for parallelizing challenging applications. Recent work has shown that speculative parallelism is plentiful for a large class of applications that have traditionally been hard to parallelize. However, adding hardware support for speculative parallelism to a throughput-oriented system leads to a severe pathology: aborted work consumes scarce resources and hurts the throughput...

  12. Computational bounce flash for indoor portraits

    Murmann, Lukas
    Portraits taken with direct flash look harsh and unflattering because the light source comes from a small set of angles very close to the camera. Advanced photographers address this problem by using bounce flash, a technique where the flash is directed towards other surfaces in the room, creating a larger, virtual light source that can be cast from different directions to provide better shading variation for 3D modeling. However, finding the right direction to point a bounce flash towards requires skill and careful consideration of the available surfaces and subject configuration. Inspired by the impact of automation for exposure, focus...

  13. Identifying patients at high risk of death with novel computational biomarkers

    Myers, Paul Daniel
    The accurate assessment of a patient's risk of adverse events remains a mainstay of clinical care for patients with cardiovascular disease. Commonly used risk metrics have traditionally been based on simple models that incorporate various aspects of the medical history, presenting signs and symptoms, and lab values. More sophisticated methods, such as those based on signal processing and machine learning, form an attractive platform to build improved risk metrics because they can offer deeper insights into aspects of clinical data that cannot be approached by simpler methods. In particular, generalized additive models can exhibit comparable or superior performance to conventional...

  14. Neural adaptive video streaming with pensieve

    Mao, Hongzi
    Client-side video players employ bitrate adaptation algorithms to cater to the ever-growing QoE requirements of users. These ABR algorithms must balance multiple QoE factors, such as maximizing video bitrate and minimizing rebuffering times. Despite the abundance of recently proposed ABR algorithms, state-of-the-art schemes suffer from two practical challenges: (1) throughput prediction is difficult and inaccurate predictions can lead to degraded performance; (2) existing algorithms use fixed heuristics which have been fine-tuned according to strict assumptions about deployment environments - such tuning precludes generalization across network conditions and QoE objectives. To overcome these challenges, we develop Pensieve, a system that generates...

  15. Large scale applications of 2D materials for sensing and energy harvesting

    McVay, Elaine D
    In this project we demonstrate the fabrication and characterization of printed reduced graphene oxide strain sensors, Chemical Vapor Deposition (CVD) 2D material transistors, and tungsten diselenide (WSe₂) photovoltaic devices that were produced through a combination of printing and conventional microfabrication processes. Each of these components were designed with the purpose of fitting into a "smart skin" system that could be discretely integrated into and sense its environment. This thesis document will describe the modification-of a 3D printer to give it inkjet capabilities that allow for the direct deposition of graphene oxide flakes onto a 3D printed surface. These graphene oxide...

  16. Learning the probability of activation in the presence of latent spreaders

    Makar, Maggie, S.M. Massachusetts Institute of Technology
    When an infection spreads among members of a community, an individual's probability of becoming infected depends on both his susceptibility to the infection and exposure to the disease through contact with others. While one often has knowledge regarding an individual's susceptibility, in many cases, whether or not an individual's contacts are contagious and spreading the infection is unknown or latent. We propose a new generative model in which we model the neighbors' spreader states and the individuals' exposure states as latent variables. Combined with an individual's characteristics, we estimate the risk of infection as a function of both exposure and...

  17. Unsupervised learning of morphological forests

    Luo, Jiaming, S.M. Massachusetts Institute of Technology
    This thesis focuses on unsupervised modeling of morphological families, collectively comprising a forest over the language vocabulary. This formulation enables us to capture edge-wise properties reflecting single-step morphological derivations, along with global distributional properties of the entire forest. These global properties constrain the size of the affix set and encourage formation of tight morphological families. The resulting objective is solved using Integer Linear Programming (ILP) paired with contrastive estimation. We train the model by alternating between optimizing the local log-linear model and the global ILP objective. We evaluate our system on three tasks: root detection, clustering of morphological families and...

  18. Temporal registration for MRI time series

    Liao, Ruizhi
    Time-course analysis in medical image series often suffers from serious motion. Registration provides voxel correspondences among images, and is commonly employed for correcting motion in medical images. Yet, the registration procedure fails when aligning volumes that are substantially different from template. We present a robust method to correct for motion and deformations in MRI time series. We make a Markov assumption on the nature of deformations to take advantage of the temporal smoothness in the image data. Forward message passing in the corresponding hidden Markov model (HMM) yields an estimation algorithm that only has to account for relatively small motion...

  19. Towards more biologically plausible deep learning and visual processing

    Liao, Qianli
    Over the last decade, we have witnessed tremendous successes of Artificial Neural Networks (ANNs) on solving a wide range of Al tasks. However, there is considerably less development in understanding the biological neural networks in primate cortex. In this thesis, I try to bridge the gap between artificial and biological neural networks. I argue that it would be beneficial to build ANNs that are both biologically-plausible and well-performing, since they may serve as models for the brain and guide neuroscience research. On the other hand, developing a biology-compatible framework for ANNs makes it possible to borrow ideas from neuroscience to...

  20. Characterization of nanostructured hexagonal boron nitride patterned via high-resolution ion beam lithography

    López, Josué Jacob
    The forefront of polariton research in two-dimensional (2D) materials focuses on pushing the limits of patterning 2D materials into nanoresonators and other nanophotonic structures that manipulate highly confined polaritons for technologically relevant near-IR and mid-IR applications. Furthermore, tuning the properties of hexagonal boron nitride, graphene, and other 2D materials in-plane and stacking them into heterostructures has the potential to create hybrid optical, electronic, thermal, and mechanical properties with a wealth of new functions. To fully tailor these novel properties, controlled nanoscale patterning of these and other van der Waals materials is essential. Moreover, it becomes imperative to understand how patterning...

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