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Electrical Engineering and Computer Sciences - Master's degree

Mostrando recursos 1 - 20 de 6.051

  1. Deployment of a next generation networking protocol

    Mercer, Logan (Logan James McClure)
    This thesis presents experimental verification of the performance of Group Centric Networking (GCN), a next generation networking protocol developed for robust and scalable communications in lossy networks where users are localized to geographic areas, such as military tactical networks. In previous work, initial simulations in NS3 showed that GCN offers high delivery with low network overhead in the presence of high packet loss and high mobility. We extend this prior work to verify GCN's performance in actual over-the-air experimentation. In the experiments, we deployed GCN on a 90-node Android phone test bed that was distributed across an office building, allowing...

  2. Design of optoelectronic activation, local memory and weighting circuits for Compact Integrated Optoelectronic Neural (COIN) Co-processor

    Tadele, Wegene Haile
    The Compact Integrated Optoelectronic Neural (COIN) Co-processor, a prototype of artificial neural network implemented in hybrid optics and optoelectronic hardware, aims to implement a multi-layer neural network algorithm by performing parallel and efficient neural computations. In this thesis, we design and implement optoelectronic thresholding (activation), weighting and memory circuits for the COIN processor. The first version involved the design of fixed thresholding and weighting functions. The second version incorporated a local capacitive memory element as well as variable weighting schemes. The third version introduces an additional flexibility for variable thresholding by changing the bias voltages of control transistors. A 9x9...

  3. A fast analog controller for a unity power factor AC/DC converter

    Eissa, Mohamed O. (Mohamed Omer)
    by Mohamed O. Eissa.

  4. Failure detection in transformers using vibrational analysis

    Lavalle, Juan Carlos
    by Juan Carlos Lavalle.

  5. Algorithms for E. coli genome engineering

    Patel, Muneeza S
    Author summary: Lamba red recombineering is one of methods of performing genome engineering. However, this method of genome editing is not very specific and efficient and is highly dependent on the genomic regions that are targeted (integration sites). In this project we explored ways of identifying what makes a site well suited for lambda red genome engineering. We wanted to explore whether we can eventually predict the "goodness" of an integration site using an algorithm. Our initial approach to the problem was to write an algorithm based on some characteristics that we felt would be key to determining the goodness...

  6. Program auto-tuning through population-based stochastic optimization

    Zhan, Minshu
    Program optimization often can and need to be decoupled from the programs innate logic. In various domains, program autotuners have been developed which can automatically search for optimal program configurations using established optimization methods. OpenTuner is a general-purpose autotuner which provides a flexible search domain representation and robust optimization techniques. This thesis extends Open-Tuner with the family of population-based stochastic optimization (PBSO) algorithms to boost OpenTuner's search capability on discrete-valued tuning problems.

  7. Viewer interface for first person shooter streaming

    Yeung, Eva M
    Although eSports has stepped into the realm of mainstream entertainment, the first person shooters still lag behind other types of games in terms of viewer consumption. This is partly due to the genre's fast paced nature, which makes it difficult for new users to pick up or for experienced users to explain effectively during a stream; thereby limiting its accessibility to the general public. This paper presents an implementation of an interface, based on Epic's Unreal Tournament's spectator mode, which helps mitigate these problems by providing stream casters a way to guide their audience better through a more informative HUD...

  8. Disentangled representations in neural models

    Whitney, William, M. Eng (William F.) Massachusetts Institute of Technology
    Representation learning is the foundation for the recent success of neural network models. However, the distributed representations generated by neural networks are far from ideal. Due to their highly entangled nature, they are difficult to reuse and interpret, and they do a poor job of capturing the sparsity which is present in real-world transformations. In this paper, I describe methods for learning disentangled representations in the two domains of graphics and computation. These methods allow neural methods to learn representations which are easy to interpret and reuse, yet they incur little or no penalty to performance. In the Graphics section,...

  9. Eulerian-Lagrangian stereo-to-multi-view Conversion

    Wang, Szu-Po
    In contrast to the popularity of stereoscopic 3D (S3D) movies in movie theaters, the adoption of S3D at home is low. It is widely believed that watching S3D with glasses is not the pratical approach for a home setting. A much more appealing approach is to use automultiscopic displays that provide a glasses-free 3D experience to multiple viewers. The main technical challenge that hampers the adaptation of this technology is the lack of multi-view content. We develop a real-time system that converts stereoscopic video to a high-quality, multi-view video, which can be directly fed to automultiscopic displays. Our algorithm uses...

  10. A tool for automated inference in rule-based biological models

    Voss, Chelsea (Chelsea S.)
    Rule-based biological models help researchers investigate systems such as cellular signalling pathways. Although these models are generally programmed by hand, some research efforts aim to program them automatically using biological facts extracted from papers via natural language processing. However, NLP facts cannot always be directly converted into mechanistic reaction rules for a rule-based model. Thus, there is a need for tools that can convert biological facts into mechanistic rules in a logically sound way. We construct such a tool specifically for Kappa, a model programming language, by implementing Iota, a logic language for Kappa models. Our tool can translate biological...

  11. A distributed metadata-private messaging system

    Tyagi, Nirvan
    Private communication over the Internet continues to be a difficult problem. Even if messages are encrypted, it is hard to deliver them without revealing metadata about which pairs of users are communicating. Scalable systems such as Tor are susceptible to traffic analysis. In contrast, the largest-scale systems with metadata privacy require passing all messages through a single server, which places a hard cap on their scalability. This paper presents Stadium, the first system to protect both messages and metadata while being able to scale its work efficiently across multiple servers. Stadium uses the same differential privacy definition for metadata privacy...

  12. Development of a platform for sensing cellular electrical activity using nitrogen vacancy centers in nanodiamonds

    Anubhav Sinha, Anubhav, M. Eng. Massachusetts Institute of Technology
    The nitrogen vacancy center (NV) in diamond hosts unique optical properties that allows it to be used for sensing magnetic fields, electric fields, and temperature. In addition, the photostability of the NV center and the biocompatibility of diamond suggests the utility of the NV center for biosensing. The dependence of the charge state of the NV center on the local electrochemical environment suggests that the NV center could be used as an optical sensor for electrophysiology. In this thesis, a platform to evaluate the utility of the NV center for voltage sensing is established. First, an electrophysiology setup is built...

  13. HalideTuner : generating and tuning halide schedules with Opentuner

    Zingales, Giuseppe Paolo Ernesto Toffanin
    Halide is a programming language designed to make it easier to write high-performance image processing code. Halide separates the scientific problem of how to write the image processing algorithm to get the required result from the engineering problem of how to schedule the different parts of this algorithm to get a fast solution. This allows Halide's users to test out different schedules for a single image processing algorithm to get the fastest program. Unfortunately, the amount of possible schedules is staggeringly large, and there is no deterministic way to find the fastest schedules. This leads to a lot of man-hours...

  14. Efficient semantic retrieval on K-segment coresets of user videos

    Kandel, Pramod
    Every day, we collect and store various kinds of data with our modern sensors, phones, cameras, and various gadgets. One of the richest available data is video data. We take numerous hours of videos with our phones and cameras, and store them in computers or cloud. However, because recording videos produce large files, it is hard to search and locate for specific video segments within a video library. We might need the part where "Matt was playing guitar", or we might want to see "the glimpse of John's laptop" among hours of video data that contain those pieces. The goal...

  15. Point cloud segmentation for mobile robot manipulation

    Zhu, Charlotte
    In this thesis, we develop a system for estimating a belief state for a scene over multiple observations of the scene. Given as input a sequence of observed RGB-D point clouds of a scene, a list of known objects in the scene and their pose distributions as a prior, and a black-box object detector, our system outputs a belief state of what is believed to be in the scene. This belief state consists of the states of known objects, walls, the floor, and "stuff" in the scene based on the observed point clouds. The system first segments the observed point...

  16. Spectral analysis of high-frequency finance

    Zhang, Yuqing, M. Eng Massachusetts Institute of Technology
    Popular across a wide range of fields, spectral analysis is a powerful technique for studying the behavior of complex systems. It decomposes a signal into many different periodic components, each associated with a specific cycle length. We argue that the application of spectral analysis to finance leads to natural interpretations in terms of horizon-specific behaviors. A spectral framework provides a few main advantages over conventional time domain approaches to financial analysis: (1) improved computational efficiency for the evaluation of behaviors across a spectrum of time horizons, (2) reduced vulnerability to aliasing effects, and (3) more convenient representations of inherently cyclic...

  17. Designing a SQL query rewriter to enforce database Row Level Security

    Zhang, Xiao Meng, M. Eng Massachusetts Institute of Technology
    This thesis presents the design and implementation of Row Level Security, a fine-grained access control mechanism built on top of a database-agnostic data sharing platform called DataHub. Existing access control mechanisms for database systems are typically coarse-grained, in the sense that users are either given access to an entire database table or nothing at all. This is problematic with the rise in popularity of data sharing, where users want to share subsets of data in a table with others, rather than the entire table. Row Level Security addresses this problem by allowing users to create security policies that define subsets...

  18. Modeling user network transitions : various approaches

    Xu, Shidan, M. Eng Massachusetts Institute of Technology
    This project involves learning to predict users' mobility within the network topology. Topological mobility, as opposed to physical mobility, can be substantial as a user switches from LTE to wifi network, while moving minimally physically. Our dataset consists of email IMAP logs as they document associated client IP addresses, as well as the clients' identifiers. Prediction for online mobility is of particular interest to the networks community. If we can predict online mobility with high probability, then new network architecture can be designed to optimize the caching system by minimizing resending packets. We used various approaches and techniques to model...

  19. Progression of computational thinking skills demonstrated by App Inventor users

    Xie, Benjamin Xiang-Yu
    analyze skill progression in MIT App Inventor, an open, online learning environment with over 4.7 million users and 14.9 million projects/apps created. My objective is to understand how people learn computational thinking concepts while creating mobile applications with App Inventor. In particular, I am interested in the relationship between the development of sophistication in using App Inventor functionality and the development of sophistication in using computational thinking concepts as learners create more apps. I take steps towards this objective by modeling the demonstrated sophistication of a user along two dimensions: breadth and depth of capability. Given a sample of 10,571...

  20. Private k-means clustering : algorithms and applications

    Xiang, Chongyuan
    Today is a new era of big data. We contribute our personal data for the common good simply by using our smart phones, searching the web and doing online transactions. Researchers, companies and governments use the collected data to learn various user behavior patterns and make impactful decisions based on that. Is it possible to publish and run queries on those databases without disclosing information about any specific individual? Differential privacy is a strong notion of privacy which guarantees that very little will be learned about individual records in the database, no matter what the attackers already know or wish...

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