459 research outputs found
RF-IDraw: virtual touch screen in the air using RF signals
Get PDFPrior work in RF-based positioning has mainly focused on discovering the absolute location of an RF source, where state-of-the-art systems can achieve an accuracy on the order of tens of centimeters using a large number of antennas. However, many applications in gaming and gesture based interface see more benefits in knowing the detailed shape of a motion. Such trajectory tracing requires a resolution several fold higher than what existing RF-based positioning systems can offer.
This paper shows that one can provide a dramatic increase in trajectory tracing accuracy, even with a small number of antennas. The key enabler for our design is a multi-resolution positioning technique that exploits an intrinsic tradeoff between improving the resolution and resolving ambiguity in the location of the RF source. The unique property of this design is its ability to precisely reconstruct the minute details in the trajectory shape, even when the absolute position might have an offset. We built a prototype of our design with commercial off-the-shelf RFID readers and tags and used it to enable a virtual touch screen, which allows a user to interact with a desired computing device by gesturing or writing her commands in the air, where each letter is only a few centimeters wide.Lincoln LaboratoryUnited States. Air Forc
Eliminating Channel Feedback in Next-Generation Cellular Networks
No full textThis paper focuses on a simple, yet fundamental question: ``Can a node infer the wireless channels on one frequency band by observing the channels on a different frequency band?'' This question arises in cellular networks, where the uplink and the downlink operate on different frequencies. Addressing this question is critical for the deployment of key 5G solutions such as massive MIMO, multi-user MIMO, and distributed MIMO, which require channel state information.
We introduce R2-F2, a system that enables LTE base stations to infer the downlink channels to a client by observing the uplink channels from that client. By doing so, R2-F2 extends the concept of reciprocity to LTE cellular networks, where downlink and uplink transmissions occur on different frequency bands. It also removes a major hurdle for the deployment of 5G MIMO solutions. We have implemented R2-F2 in software radios and integrated it within the LTE OFDM physical layer. Our results show that the channels computed by R2-F2 deliver accurate MIMO beamforming (to within 0.7~dB of beamforming gains with ground truth channels) while eliminating channel feedback overhead
Caraoke: An E-Toll Transponder Network for Smart Cities
Get PDFElectronic toll collection transponders, e.g., E-ZPass, are a widely-used wireless technology. About 70 % to 89 % of the cars in US have these devices, and some states plan to make them mandatory. As wireless devices however, they lack a basic function: a MAC protocol that prevents colli-sions. Hence, today, they can be queried only with direc-tional antennas in isolated spots. However, if one could in-teract with e-toll transponders anywhere in the city despite collisions, it would enable many smart applications. For ex-ample, the city can query the transponders to estimate the ve-hicle flow at every intersection. It can also localize the cars using their wireless signals, and detect those that run a red-light. The same infrastructure can also deliver smart street-parking, where a user parks anywhere on the street, the city localizes his car, and automatically charges his account. This paper presents Caraoke, a networked system for de-livering smart services using e-toll transponders. Our design operates with existing unmodified transponders, allowing for applications that communicate with, localize, and count transponders, despite wireless collisions. To do so, Caraoke exploits the structure of the transponders ’ signal and its prop-erties in the frequency domain. We built Caraoke reader into a small PCB that harvests solar energy and can be easily de-ployed on street lamps. We also evaluated Caraoke on four streets on our campus and demonstrated its capabilities
Sub-nanosecond time of flight on commercial Wi-Fi cards
No full textThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.Cataloged from PDF version of thesis.Includes bibliographical references (pages 49-52).Time-of-flight, i.e., the time incurred by a signal to travel from transmitter to receiver, is perhaps the most intuitive way to measure distances using wireless signals. It is used in major positioning systems such as GPS, RADAR, and SONAR. However, attempts at using time-of-flight for indoor localization have failed to deliver acceptable accuracy due to fundamental limitations in measuring time on Wi-Fi and other RF consumer technologies. While the research community has developed alternatives for RF-based indoor localization that do not require time-of-flight, those approaches have their own limitations that hamper their use in practice. In particular, many existing approaches need receivers with large antenna arrays while commercial Wi-Fi nodes have two or three antennas. Other systems require fingerprinting the environment to create signal maps. More fundamentally, none of these methods support indoor positioning between a pair of Wi-Fi devices without third party support. In this thesis, we present a set of algorithms that measure the time-of-flight to sub-nanosecond accuracy on commercial Wi-Fi cards. We implement these algorithms and demonstrate a system that achieves accurate device-to-device localization, i.e. enables a pair of Wi-Fi devices to locate each other without any support from the infrastructure, not even the location of the access points.by Deepak Vasisht.S.M
Towards realizing the internet-of-things vision : in-body, homes, and farms
No full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 171-187).The Internet-of-things (IoT) enables us to connect our physical and digital worlds by embedding computing devices into our environment. Today, there is a huge interest in IoT systems for smart homes, smart cities, digital healthcare, data-driven agriculture, etc. However, for these IoT systems to deliver their intended vision, we need to address two important challenges: (a) operation under limited resources like power and connectivity, (b) operation in spite of extreme heterogeneity in device deployments. In this thesis, we address both these challenges. We design a new communication primitive that allows inaccessible resource-constrained devices like in-body devices to communicate without requiring them to transmit any power of their own. To address heterogeneity, we present two approaches. First, we build a teacher-student model for IoT systems which allows us to train models that can learn to predict one sensor modality from another. This makes IoT systems more robust to failures, enables more accurate inference, and reduces deployment costs. Second, we build a formal model that embeds contextual information about the environment into the inference process and allows heterogenous devices to perform joint inference that is more accurate and robust than either of the devices alone. We demonstrate the efficacy of our approach through end-to-end systems developed for diverse environments with varying constraints on size, power, communication, and sensing modalities: inside the human body, smart homes, and agricultural farms. We deploy these systems for long-term in real world environments and present our insights from these deployments. Finally, we demonstrate that the techniques developed in this thesis have general applicability beyond the application scenarios themselves, for example, in next generation cellular communications.by Deepak Vasisht.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc
Annotating RFID-attached objects in images
No full textSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01The student, Emerson Sie, accepted the attached license on 2024-04-26 at 15:15.The student, Emerson Sie, submitted this Thesis for approval on 2024-04-26 at 15:20.This Thesis was approved for publication on 2024-04-30 at 15:07.DSpace SAF Submission Ingestion Package generated from Vireo submission #20600 on 2024-09-16 at 00:44:28Wireless tags are increasingly used to track and identify common items of interest such as retail goods, food, medicine, clothing, books, documents, keys, equipment, and more. At the same time, there is a need for labelled visual data featuring such items for the purpose of training object detection and recognition models for robots operating in homes, warehouses, stores, libraries, pharmacies, and so on. In this thesis, we ask: can we leverage the tracking and identification capabilities of such tags as a basis for a large-scale automatic image annotation system for robotic perception tasks? We present RF-Annotate, a pipeline for autonomous pixelwise image annotation which enables robots to collect labelled visual data of objects of interest as they encounter them within their environment. Our pipeline uses unmodified commodity RFID readers and RGB-D cameras, and exploits arbitrary small-scale motions afforded by mobile robotic platforms to spatially map RFIDs to corresponding objects in the scene. Our only assumption is that the objects of interest within the environment are pre-tagged with inexpensive battery-free RFIDs costing 3--15 cents each. We demonstrate the efficacy of our pipeline on several RGB-D sequences of tabletop scenes featuring common objects in a variety of indoor environments
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Relational Care - with Mary Larkin and Manik Deepak-Gopinath [Podcast]
No full textWhat is 'relational care' and how can it improve the day-to-day experience of carers and those they care for? What are its implications for relationships between staff and service users in care settings? And how does the concept of relational care enable us to re-imagine the role of place and space in the experience of care? These are some of the questions we explore in this episode with Mary Larkin and Manik Deepak-Gopinath who recently completed a research project on the value and practice of relational care with older people.
Mary is Professor of Care, Carers and Caring at The Open University in the UK, where her research has focused on carers and caring and adult social care. She is the co-author, most recently of Family Carers and Caring, published in 2023 by Emerald. Manik is a Lecturer in Ageing, also at The Open University, and is a critical gerontologist with interests in the intersection of ageing, place and wellbeing, and in the intimate and family ties of older adults
My Name Is Deepak
No full textThis chapter looks at the author's responses to being given a nickname by his co-workers: Tupac. They do it in a friendly manner, but the author doesn’t understand the connection with the American rapper. It makes him think about who he is, his identity, and how people see him in his adopted country.</p
Privacy against unsolicited radio-frequency sensing using machine learning
Get PDFSubmission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2025-12-01The student, Zikun Liu, accepted the attached license on 2023-12-07 at 23:21.The student, Zikun Liu, submitted this Thesis for approval on 2023-12-07 at 23:27.This Thesis was approved for publication on 2023-12-08 at 08:21.DSpace SAF Submission Ingestion Package generated from Vireo submission #20173 on 2024-03-01 at 13:32:44In the last decade, both academia and industry have relied on passive radio frequency (RF) sensing to enable new capabilities for smart devices. Passive RF sensors can capture radio signal reflections from human bodies to track occupancy of rooms [1], motion patterns of occupants [2], and in more advanced systems, the breathing [3, 4], heart rate [5], sleeping patterns [6], keystrokes [7], and even emotions of occupants [8]. Recently, Google has incorporated such passive sensing into their smart home devices [9, 10] and Amazon received an FCC waiver [11] to conduct testing for the same. Notably, such sensors work without requiring users to carry a device and operate successfully through walls and other obstacles. Such tracking opens up a completely new set of privacy challenges. Smart devices can sense and mine behavioral data, passers-by can see if a home is empty, and neighbors can eavesdrop on your activities. Walls, curtains, and doors are meant to offer privacy to our indoor spaces. However, this notion of privacy no longer holds in the presence of RF-based sensing mechanisms that sense this information through walls. What makes this even more challenging is that it is near impossible for humans to evade such sensing. Human bodies naturally interact with radio signals and create small modifications that are then used for tracking. We propose a new framework for enabling privacy and user-control in the context of RF sensing – privacy by hallucination. We build new hardware-software techniques to inject fake ‘ghost’ reflections that appear like humans. By injecting false data and controlling false data injections, we can corrupt sensed information and allow users to regain control of private spaces. Part of the text is derived from [12]
Sideffective - system to mine patient reviews: sentiment analysis
No full textSideffective is the system to crawl, rank and analyze patient testimonials about side ffeects from common medications. Since the wealth of any mining model is the Data corpus, the data collection phase involved extensive crawling of massive medical websites comprised of user forums from the internet. Subsequently, the raw files were subjected to certain site-specific parsing routines, yielding outputs conforming to a well-defined data model. Currently, the system holds close to 400,000 user testimonials pertaining to more than 2500 drugs/medicines. Sideffective aims at gathering and aggregating this wealth of information, build useful associations and present interesting observations and numeric validations, all in a user-friendly interface. The important issues that we have tried to tackle are: Extracting side effects without relying on pre-built lists, aggregating distribution of different side effect for a give drug, site-specific search, ranking and determining the negativity of reviews. The system has been jointly built by Deepak Yalamanchi and Sangeetha Rajagopalan under the guidance of Prof. Tomasz Imielinski. This thesis focuses mainly on Sentiment Analysis of patient reviews. While most existing sentiment analysis systems are predicated by POS (parts of speech) tagging or Bayesian sentiment analysis methods, the same cannot be applied to medical reviews as they generally carry a negative flavor in them. We thereby approached the problem by identifying the features in the sentence and calibrating the sentiment on a Negativity Meter based on their relation to sentiment words. A feature, as defined for the purpose of this thesis, can be a medicine, a side effect or a symptom. The sentiment of each feature is determined by the aggregate of all its polarities with respect to each sentiment word, where the polarity is determined by an inverse relation to the distance of the feature from the sentiment word. Each sentence is then evaluated by the cumulative polarity of all the features contained in it. Sentiment of a review is determined by individually determining the sentiment of each sentence and then getting a weighted sum score of all the sentences in the review. The accuracy of a sentiment analysis system is, in principle, how well it agrees with human judgments. Experimental results, involving human reviewers (extracted from site: www.askapatient.com) and correlating them back to the negativity rating of each review yield conclusive results, demonstrating the effectiveness of the technique. We have also implemented a customized Lucene search on the data using a multi-review summarization approach and a ranking scheme based on the feature-list. Ranking priority is given to the review that has the largest feature list size.M.S.Includes bibliographical referencesby Deepak Yalamanch
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