Electrical Engineering and Computer Sciences - Master's degree
http://hdl.handle.net/1721.1/7817
2017-11-07T19:22:41ZEmotion recognition using wireless signals
http://hdl.handle.net/1721.1/112055
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 accuracy is on par with state-of-the-art emotion recognition systems that require a person to be hooked to an ECG monitor.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 47-52).
2017-01-01T00:00:00ZPrivate sequential search and optimization
http://hdl.handle.net/1721.1/112054
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 optimal solution, x*, while simultaneously ensuring privacy, by making x* difficult to infer for the data provider, who does not know the agent's private weights but only observes the agent's queries. 2. The second private search model we study is also about protecting privacy while searching for an object. It involves an agent attempting to determine a scalar true value, x*, based on querying an external database, whose response indicates whether the true value is larger than or less than the agent's submitted queries. The objective of the agent is again to obtain an accurate estimate of the true value, x*, while simultaneously hiding it from an adversary who observes the submitted queries but not the responses. The main results of this thesis provide tight upper and lower bounds on the agent's query complexity (i.e., number of queries) as a function of desired levels of accuracy and privacy, for both models. We also explicitly construct query strategies whose worst-case query complexity is optimal up to an additive constant.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 107-108).
2017-01-01T00:00:00ZOn-demand high-capacity ride-sharing via dynamic trip-vehicle assignment with rebalancing
http://hdl.handle.net/1721.1/112051
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 for low to medium capacity vehicles. We validated the algorithm with over three million taxi rides from the New York City taxi dataset and demonstrate that our approach can service nearly 99% of Manhattan taxi demand using a fleet of only 3000 vehicles (less than 25% of the active taxis in Manhattan).
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 61-64).
2017-01-01T00:00:00ZFaster algorithms for matrix scaling and balancing via convex optimization
http://hdl.handle.net/1721.1/112050
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 whenever K is quasi-polynomial, which includes, in particular, the case of strictly positive matrices. We complement our results by providing a separate algorithm that uses an interior-point method and runs in time Õ(m³/²(log log K + log(1/[epsilon]))), which becomes Õ(m³/² log(1/[epsilon])) for the case of matrix balancing and the doubly-stochastic variant of matrix scaling. In order to establish these results, we develop a new second-order optimization framework that enables us to treat both problems in a unified and principled manner. This framework identifies a certain generalization of linear system solving which we can use to efficiently minimize a broad class of functions, which we call second-order robust. We then show that in the context of the specific functions capturing matrix scaling and balancing, we can leverage and generalize the work on Laplacian system solving to make the algorithms obtained via this framework very efficient. This thesis is based on joint work with Michael B. Cohen, Aleksandr Mądry, and Adrian Vladu.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 61-65).
2017-01-01T00:00:00Z