Frost accumulation on refrigerator evaporator coils is a significant source of wasted energy. While automatic de-frosting is a standard feature on modern refrigerators, current commercial solutions use heuristics to determine the frequency of heating cycles, leading to a sub-optimal defrosting routine. The majority of previous defrosting research incorporates cameras or microwave technology to better inform defrost algorithms of frost accumulation, however, these methods are both financially and computationally expensive. In this paper, we propose a low-cost frost detection system using ultrasonic resonance of piezoelectric sensors. We addressed the financial and computational cost challenges by using low-cost sensors and basic circuit components to replace software complexity. Our frost detection system was evaluated extensively in a Samsung refrigerator, resulting in a frost detection accuracy of 99.7%. We believe our solution can be further used for downstream refrigerator control cycle optimizations to achieve improved energy efficiency.

Robot end effectors with proximity detection and contact sensing capabilities can reactively position the gripper to align objects and ensure successful grasps. In this paper, we introduce SonicFinger, an acoustic aura based sensing system capable of full-surface pre-touch and contact sensing. A single piezoelectric transducer embedded within a novel 3D printed finger is excited using a monotone to create an acoustic aura encompassing the finger; this enables pre-touch sensing and gripper alignment, while changes in finger-transducer acoustic coupling indicate contact. SonicFinger is low-cost, compact, and easy to manufacture and assemble. Sensing capabilities are evaluated using a set of objects with various physical properties such as optical reflectivity, dielectric constants, mechanical properties, and acoustic absorption. A dataset with over 8,000 proximity and contact events is collected. Our system shows a pre-touch detection true positive rate (TPR) of 92.4% and a true negative rate (TNR) of 95.3%. Contact detection experiments show a TPR of 93.7% and a TNR of 98.7%. Furthermore, pretouch detection information from Sonic Finger is used to adjust the robot grippers pose to align a target object at the center of both fingers.

Contact sensing and localization capabilities that mimic human skin are highly desirable for robots. In this paper, we introduce AcouSkin, an acoustic wave based full surface contact localization system. Acoustic waves produced by piezoelectric transceivers using a monotone are coupled to surfaces turning them into an active sensor. Our system leverages information from four piezoelectric transceivers mounted on the surface of an acrylic sheet and vacuum cleaner robot bumper to localize contacts to 18 unique segments. We first characterize acoustic wave propagation based on signal and material properties and then propose hardware and software methods to realize full surface contact localization. Our results show that AcouSkin can reliably localize contact on a flat acrylic sheet with 18 uniformly spaced locations across a 54cm length with mean absolute error (MAE) of ≤ 1 locations using maximum likelihood estimator (MLE) and multilayer perceptron (MLP) models. On the vacuum cleaner robot bumper AcouSkin shows a zero MAE. Further, the system is also able to localize contacts made using forces as low as 2N (Newtons) and as high as 20N. Overall, AcouSkin provides full surface contact localization while requiring minimal instrumentation with easy deployment on real-world robots.

In this paper, we present AmbiSense, an acoustic field based sensing system that performs proximity detection and bearing estimation for safer physical human-robot interactions. A single low cost piezoelectric transducer is used to setup this novel acoustic sensing modality to create a blindspot-free sound field engulfing a robot arm. Two detection algorithms leveraging spectral information from reflected audio waves of objects entering the acoustic field are proposed to infer object presence and bearing. We also present a new receiver structure which improves signal to noise ratio (SNR). AmbiSense is paired with a collision avoidance inverse kinematic solver for real world deployment on a Kinova Gen3 robot. Validation is performed using ten test objects generating 2000 proximity and bearing estimation events in real world settings, we show that AmbiSense detects proximity with 93.8% sensitivity and 96.6 % specificity. It estimates bearing and maps it to three zones on a robot link with 100% sensitivity and specificity, while using fewer sensors than state of the art methods for similar coverage.

This paper presents Audioplethysmography (APG), a novel cardiac monitoring modality for active noise cancellation (ANC) headphones. APG sends a low intensity ultrasound probing signal using an ANC headphone's speakers and receives the echoes via the on-board feedback microphones. We observed that, as the volume of ear canals slightly changes with blood vessel deformations, the heartbeats will modulate these ultrasound echoes. We built mathematical models to analyze the underlying physics and propose a multi-tone APG signal processing pipeline to derive the heart rate and heart rate variability in both constrained and unconstrained settings. APG enables robust monitoring of cardiac activities using mass-market ANC headphones in the presence of music playback and body motion such as running.

Medication adherence is one of the leading factors that can make the difference between life and death, especially for patients managing chronic conditions [2]. Indeed, these issues have driven a recent wave of research, including the development of smart pill bottles that monitor when a pill is extracted. In this poster, we extend our recent work [1], where we present adaptive learning techniques for subject identification across multiple pill bottle systems. We collect inertial signals from 10 subjects taking medication pills and encode the activity signals by transforming them into 2D texture images. Then we use pre-trained Convolutional Neural Network (CNN) models for image-based classification tasks. Our approach achieved improved differentiation capacity over existing models by using deep learning models, modified through domain adaptation and transfer learning.

Perceiving obstacles and avoiding collisions is fundamental to the safe operation of a robot system, particularly when the robot must operate in highly dynamic human environments. Proximity detection using on-robot sensors can be used to avoid or mitigate impending collisions. However, existing proximity sensing methods are orientation and placement dependent, resulting in blind spots even with large numbers of sensors. In this paper, we introduce the phenomenon of the Leaky Surface Wave (LSW), a novel sensing modality, and present AuraSense, a proximity detection system using the LSW. AuraSense is the first system to realize no-dead-spot proximity sensing for robot arms. It requires only a single pair of piezoelectric transducers, and can easily be applied to off-the-shelf robots with minimal modifications. We further introduce a set of signal processing techniques and a lightweight neural network to address the unique challenges in using the LSW for proximity sensing. Finally, we demonstrate a prototype system consisting of a single piezoelectric element pair on a robot manipulator, which validates our design. We conducted several micro benchmark experiments and performed more than 2000 on-robot proximity detection trials with various potential robot arm materials, colliding objects, approach patterns, and robot movement patterns. AuraSense achieves 100% and 95.3% true positive proximity detection rates when the arm approaches static and mobile obstacles respectively, with a true negative rate over 99%, showing the real-world viability of this system.

Headphones continue to become more intelligent as new functions (e.g., touch-based gesture control) appear. These functions usually rely on auxiliary sensors (e.g., accelerometer and gyroscope) that are available in smart headphones. However, for those headphones that do not have such sensors, supporting these functions becomes a daunting task. This paper presents HeadFi, a new design paradigm for bringing intelligence to headphones. Instead of adding auxiliary sensors into headphones, HeadFi turns the pair of drivers that are readily available inside all headphones into a versatile sensor to enable new applications spanning across mobile health, user-interface, and context-awareness. HeadFi works as a plug-in peripheral connecting the headphones and the pairing device (e.g., a smartphone). The simplicity (can be as simple as only two resistors) and small form factor of this design lend itself to be embedded into the pairing device as an integrated circuit. We envision HeadFi can serve as a vital supplementary solution to existing smart headphone design by directly transforming large amounts of existing "dumb" headphones into intelligent ones. We prototype HeadFi on PCB and conduct extensive experiments with 53 volunteers using 54 pairs of non-smart headphones under the institutional review board (IRB) protocols. The results show that HeadFi can achieve 97.2%--99.5% accuracy on user identification, 96.8%--99.2% accuracy on heart rate monitoring, and 97.7%--99.3% accuracy on gesture recognition.

The COVID-19 pandemic has impacted academic life in different ways. In the mobile and pervasive computing community, there was a struggle on data collection for the evaluation of human-sensing systems. An automated and contactless solution to collect data from users at home is one way that can help in the continuation of user-centric studies. In this poster, we present a portable system for remote, in-home data collection. The system is powered by a Raspberry Pi© and input peripherals (a camera, a microphone, and a wireless receiver). Our system uses a speech interface for text-to-speech and speech-to-text conversions. The system acts as a voice-based "smart agent" that guides the user during an experiment session. We aim to use our system to collect data from a set of smart pill bottles that we previously designed for medication adherence monitoring [1] and user identification [3].

This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer. In-N-Out can continuously charge a medical implant residing in deep tissues at near-optimal beamforming power, even when the implant moves around inside the human body. To accomplish this, we exploit the unique energy ball pattern of distributed antenna array and devise a backscatter-assisted beamforming algorithm that can concentrate RF energy on a tiny spot surrounding the medical implant. Meanwhile, the power levels on other body parts stay in low level, reducing the risk of overheating. We proto-type In-N-Out on 21 software-defined radios and a printed circuit board (PCB). Extensive experiments demonstrate that In-N-Out achieves 0.37 mW average charging power inside a 10 cm-thick pork belly, which is sufficient to wirelessly power a range of commercial medical devices. Our head-to-head comparison with the state-of-the-art approach shows that In-N-Out achieves 5.4X-18.1X power gain when the implant is stationary, and 5.3X-7.4X power gain when the implant is in motion.

Driver assistance and vehicular automation would greatly benefit from uninterrupted lane-level vehicle positioning, especially in challenging environments like metropolitan cities. In this paper, we explore whether the WiFi Fine Time Measurement (FTM) protocol, with its robust, accurate ranging capability, can complement current GPS and odometry systems to achieve lane-level positioning in urban canyons. We introduce Wi-Go, a system that simultaneously tracks vehicles and maps WiFi access point positions by coherently fusing WiFi FTMs, GPS, and vehicle odometry information together. Wi-Go also adaptively controls the FTM messaging rate from clients to prevent high bandwidth usage and congestion, while maximizing the tracking accuracy. Wi-Go achieves lane-level vehicle positioning (1.3 m median and 2.9 m 90-percentile error), an order of magnitude improvement over vehicle built-in GPS, through vehicle experiments in the urban canyons of Manhattan, New York City, as well as in suburban areas (0.8 m median and 3.2 m 90-percentile error).

The past 40 years have seen an explosion of Radio Frequency (RF) transmitters, which motivates understanding their impacts on the natural world. The European honeybee, Apis Mellifera, has been shown to sense the Earth's magnetic field. Human Radio Frequency (RF) transmitters alter this field. For example, recent work demonstrated that human-created RF interferes with the common robin's ability to orient themselves. This work proposes an experimental design to determine if honeybees can sense RF transmissions in frequencies from 1 MHz (AM radio) to 6 GHz (WiFi). We deployed a custom-designed RF bee feeder near bee hives to test honeybees' RF sensing ability.

In-bed motion detection and classification are important techniques that can enable an array of applications, among which are sleep monitoring and abnormal movement detection. In this article, we present a low-cost, low-overhead, and highly robust system for in-bed movement detection and classification that uses low-end load cells. To detect movements, we have designed a feature that we refer to as Log-Peak, which can be extracted from load cell data that is collected through wireless links in an energy-efficient manner. After detection, we set out to achieve a precise body motion classification. Toward this goal, we define nine classes of movements, and design a machine learning algorithm using Support Vector Machine, Random Forest, and XGBoost techniques to classify a movement into one of nine classes. For every movement, we have extracted 24 features and used them in our model. This movement detection/classification system was evaluated on data collected from 40 subjects who performed 35 predefined movements in each experiment. We have applied multiple tree topologies for each technique to reach their best results. After examining various combinations, we have achieved a final classification accuracy of 91.5%. This system can be used conveniently for long-term home monitoring.

Accurately accounting for medication use is important for the efficacy and safety of patients and family members. Monitoring is also important for medication adherence. This work investigates identification of persons taking medication using a sensor-equipped pill bottle. The bottle is equipped with inertial and switch sensors in both the cap and body, making the added hardware unobtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using 16 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one patient among 16 subjects with 94% accuracy, and has a 93% using a single sensor. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91%.

In this work, we investigate the identification of persons taking medication using a sensor-equipped pill-bottle. The bottle embeds inertial sensors in both the cap and body, making the added hardware un-obtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using 16 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one patient among 16 subjects with 94 % accuracy. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91 %..

Head-mounted devices (HMD) for Augmented Reality (AR) are gaining traction thanks to a growing number of applications in the areas of image guided therapy, computer aided design, cargo packing, manufacturing and digital field service. However, providing an always available, intuitive and user friendly input for these devices remains a challenging problem. This paper explores recognizing dynamic, micro finger gestures using capacitive coupling for interacting with a head-mounted device. Electrodes are attached to fingertips of users gloves and capacitive coupling among all pairs of electrodes is measured quickly to infer the real-time spatial relationship between fingers. The system is able to recognize fine, low-effort finger gestures, such as swiping, sliding, tap, double-tap. We evaluated our prototype with 14 gestures executed by 10 subjects and found a 97% accuracy of gesture recognition.

Ensuring confidentiality of communication is fundamental to securing the operation of a wireless system, where eavesdropping is easily facilitated by the broadcast nature of the wireless medium. By applying distributed beamforming among a coalition, we show that a new approach for assuring physical layer secrecy, without requiring any knowledge about the eavesdropper or injecting any additional cover noise, is possible if the transmitters frequently perturb their phases around the proper alignment phase while transmitting messages. This approach is readily applied to amplitude-based modulation schemes, such as PAM or QAM. We present our secrecy mechanisms, prove several important secrecy properties, and develop a practical secret communication system design. We further implement and deploy a prototype that consists of 16 distributed transmitters using USRP N210s in a 20×20×3m3area. By sending more than 160M bits over our system to the receiver, depending on system parameter settings, we measure that the eavesdroppers failed to decode 30%-60% of the bits cross multiple locations while the intended receiver has an estimated bit error ratio of 3×10-6.

This paper explores the feasibility of localizing and detecting activities of building occupants using visible light sensing across a mesh of light bulbs. Existing Visible Light activity sensing (VLS) techniques require either light sensors to be deployed on the floor or a person to carry a device. Our approach integrates photosensors with light bulbs and exploits the light reflected off the floor to achieve an entirely device-free and light source based system. This forms a mesh of virtual light barriers across networked lights to track shadows cast by occupants. The design employs a synchronization circuit that implements a time division signaling scheme to differentiate between light sources and a sensitive sensing circuit to detect small changes in weak reflections. Sensor readings are fed into indoor supervised tracking algorithms as well as occupancy and activity recognition classifiers. Our prototype uses modified off-the-shelf LED flood light bulbs and is installed in a typical office conference room. We evaluate the performance of our system in terms of localization, occupancy estimation and activity classification, and find a 0.89m median localization error as well as 93.7% and 93.78% occupancy and activity classification accuracy, respectively.

Academic and industry research has argued for supporting WiFi time-of-flight measurements to improve WiFi localization. The IEEE 802.11-2016 now includes a Fine Time Measurement (FTM) protocol for WiFi ranging, and several WiFi chipsets offer hardware support albeit without fully functional open software. This paper introduces an open platform for experimenting with fine time measurements and a general, repeatable, and accurate measurement framework for evaluating time-based ranging systems. We analyze the key factors and parameters that affect the ranging performance and revisit standard error correction techniques for WiFi time-based ranging system. The results confirm that meter-level ranging accuracy is possible as promised, but the measurements also show that this can only be consistently achieved in low-multipath environments such as open outdoor spaces or with denser access point deployments to enable ranging at or above 80 MHz bandwidth.

The growing number of devices we interact with require a convenient yet secure solution for user identification, authorization and authentication. Current approaches are cumbersome, susceptible to eavesdropping and relay attacks, or energy inefficient. In this paper, we propose a body-guided communication mechanism to secure every touch when users interact with a variety of devices and objects. The method is implemented in a hardware token worn on user's body, for example in the form of a wristband, which interacts with a receiver embedded inside the touched device through a body-guided channel established when the user touches the device. Experiments show low-power (uJ/bit) operation while achieving superior resilience to attacks, with the received signal at the intended receiver through the body channel being at least 20dB higher than that of an adversary in cm range.

Given the increasing popularity of mobile and wearable devices, this paper explores the potential use of inertial sensors that are widely available on mobile and wearable devices for vehicle and driver tracking. Such a capability would enable novel classes of mobile safety and assisted driving applications without relying on information or sensors in the vehicle. Although inertial sensors have been widely used in motion tracking, existing approaches cannot distinguish the motion of the vehicle and the device's motion in the vehicle. Additionally, the noise exerted from the electronic components in the vehicle and the ferromagnetic frame of the vehicle distorts the inertial sensor readings. This paper introduces a method to separately estimate the orientation of both the vehicle and the sensor by tracking the earth's magnetic field and the electromagnetic distortion from the vehicle, as measured by a magnetometer in addition to a gyroscope and an accelerometer. Specifically, the vehicle noise is used to estimate the orientation of the sensor within the vehicle while the earth's magnetic field combined with vehicle noise is used to estimate the vehicle's heading. Our on-road experiments show that the technique is able to estimate the sensor orientation with a mean error of 5.61° for the yaw angle and 3.73° for the pitch angle, as well as able to estimate the vehicle heading with a mean error of 4.12°.

Accurate and continuous ammonia monitoring is important for laboratory animal studies and many other applications. Existing solutions are often expensive, inaccurate, or unsuitable for long-term monitoring. In this work, we propose a new ammonia monitoring approach that is low-power, automatic, accurate, and wireless.

Monitoring heartbeats takes an important role to ensure a person's health and well-being. Few of the existing systems are accurate, unobtrusive, robust and easy to install at the same time. Thus, we propose a completely unobtrusive system which can detect heartbeats during sleep by sensing the weak ballistic vibrations caused by heartbeats on any bed. The system, HB-Phone, is centered around the off-the-shelf geophone sensor and can be easily installed on an existing bed. In this demo, we demonstrate that our system can detect and extract heartbeats accurately and in real time, even with the presence of noise from the environment and gross body movements during sleep.

Using geophones to sense bed vibrations caused by ballistic force has shown great potential in monitoring a person's heart rate during sleep. It does not require a special mattress or sheets, and the user is free to move around and change position during sleep. Earlier work has studied how to process the geophone signal to detect heartbeats when a single subject occupies the entire bed. In this study, we develop a system called VitalMon, aiming to monitor a person's respiratory rate as well as heart rate, even when she is sharing a bed with another person. In such situations, the vibrations from both persons are mixed together. VitalMon first separates the two heartbeat signals, and then distinguishes the respiration signal from the heartbeat signal for each person. Our heartbeat separation algorithm relies on the spatial difference between two signal sources with respect to each vibration sensor, and our respiration extraction algorithm deciphers the breathing rate embedded in amplitude fluctuation of the heartbeat signal.

The number of small wireless devices is rapidly increasing, making the radio channel efficiency in limited geographic areas (individual rooms or buildings) an important metric for MAC protocols. Many of these emerging devices have use-cases that are difficult to satisfy with current hardware solutions and channel access methods; for instance device mobility, small energy reserves, and requirements for low cost and small form factors. However, for most of these applications, such as health care monitoring or sensing, feedback to the radio device is unnecessary and unidirectional communication techniques are not only sufficient, but can also be advantageous. We propose an efficient, reliable technique for unidirectional communication, called Transmit Only (TO), that satisfies these requirements while maintaining packet throughput guarantees and reducing energy consumption. In this paper we will demonstrate the feasibility and performance of this kind of highly asymmetric, transmit-only protocol through theoretical, simulated, and experimental results.

Heartbeat monitoring during sleep is critically important to ensuring the well-being of many people, ranging from patients to elderly. Technologies that support heartbeat monitoring should be unobtrusive, and thus solutions that are accurate and can be easily applied to existing beds is an important need that has been unfulfilled. We tackle the challenge of accurate, low-cost and easy to deploy heartbeat monitoring by investigating whether off-the- shelf analog geophone sensors can be used to detect heartbeats when installed under a bed. Geophones have the desirable property of being insensitive to lower-frequency movements, which lends itself to heartbeat monitoring as the heartbeat signal has harmonic frequencies that are easily captured by the geophone. At the same time, lower-frequency movements such as respiration, can be naturally filtered out by the geophone. With carefully-designed signal processing algorithms, we show it is possible to detect and extract heartbeats in the presence of environmental noise and other body movements a person may have during sleep. We have built a prototype sensor and conducted detailed experiments that involve 43 subjects (with IRB approval), which demonstrate that the geophone sensor is a compelling solution to long-term at-home heartbeat monitoring. We compared the average heartbeat rate estimated by our prototype and that reported by a pulse oximeter. The results revealed that the average error rate is around 1.30% over 500 data samples when the subjects were still on the bed, and 3.87% over 300 data samples when the subjects had different types of body movements while lying on the bed. We also deployed the prototype in the homes of 9 subjects for a total of 25 nights, and found that the average estimation error rate was 8.25% over more than 181 hours' data.

Applications on wearable personal imaging devices, or Smart-glasses as they are called, can largely benefit from accurate and energy-efficient recognition of objects that are within the user's view. Existing solutions such as optical or computer vision approaches are too energy intensive, while low-power active radio tags suffer from imprecise orientation estimates. To address this challenge, this paper presents the design, implementation, and evaluation of a radio-optical hybrid system where a radio-optical transmitter, or tag, whose radio-optical beacons are used for accurate relative orientation tracking of tagged objects by a wearable radio-optical receiver. A low-power radio link that conveys identity is used to reduce the battery drain by synchronizing the radio-optical transmitter and receiver so that extremely short optical (infrared) pulses are sufficient for orientation (angle and distance) estimation. Through extensive experiments with our prototype we show that our system can achieve orientation estimates with 1-to-2 degree accuracy and within 40 cm ranging error, with a maximum range of 9 m in typical indoor use cases. With a tag and receiver battery power consumption of 81 μW and 90 mW, respectively, our radio-optical tags and receiver are at least 1.5x energy efficient than prior works in this space.

Radio frequency based device-free localization has been proposed as an alternative localization technique. Unlike its active localization counterpart, it does not require subjects to wear any radio device, but tries to determine the subject's location by observing how much the subject disturbs the radio propagation patterns. This problem is very challenging due to the well known multipath effect, especially in a complex indoor environment where it is impractical to accurately model the effects of a subject on the surrounding radio links. In this article, we formulate the device-free localization problem using probabilistic classification approaches that are based on discriminant analysis.To boost the localization accuracies, we adopt methods to mitigate errors caused by the multipath effect, as well as methods to automatically recalibrate training data so that accuracy can be maintained as the environment evolves. We validate our method in a one-bedroom apartment that consists of 32 cells, using eight fixed transmitters and eight fixed receivers. When the space has a single occupant, our method can correctly estimate the occupied cell with a likelihood as high as 97.2 percent. Further, we show that we can maintain a high localization accuracy, while substantially reducing the deployment overhead, which is an important concern for device-free localization methods. To achieve this goal, we have improved our training and testing procedures to reduce the overhead, studied the radio device placement to optimize the device cost, devised algorithms to extend the lifetime of the training data, and designed a set of auxiliary sensors and incorporate them into the system to achieve automatic re-calibration.

Object recognition on wearable devices using computer vision is too energy intensive and challenging when objects are similar looking, while low-power active radio frequency identification (RFID) systems suffer from imprecise orientation (angle and distance) estimates. To address this challenge, this paper presents a novel radio-optical based recognition system where a radio-optical transmitter, or tag, that emits a beacon whose infra-red (IR) signal strength is used for accurate relative orientation tracking of tagged objects at a wearable radio-optical receiver. A low-power radio link that conveys identity is used to reduce the battery drain by synchronizing the radio- optical transmitter and receiver so that extremely short optical pulses are sufficient for precise orientation estimation. Through extensive experiments with our prototype we show that our system can achieve orientation estimates with 1-2° accuracy and within 40cm ranging error, with a maximum range of 9m in typical indoor use cases. With a tag battery power consumption of 86μW, the radio-optical tags show potential to achieve about half a decade lifetimes.

Ubiquitous light emitting devices and low-cost commercial digital cameras facilitate optical wireless communication system such as visual MIMO where handheld cameras communicate with electronic displays. While intensity-based optical communications are more prevalent in camera-display messaging, we present a novel method that uses modulated light phase for messaging and time-of-flight (ToF) cameras for receivers. With intensity-based methods, light signals can be degraded by reflections and ambient illumination. By comparison, communication using ToF cameras is more robust against challenging lighting conditions. Additionally, the concept of phase messaging can be combined with intensity messaging for a significant data rate advantage. In this work, we design and construct a phase messaging array (PMA), which is the first of its kind, to communicate to a ToF depth camera by manipulating the phase of the depth camera's infrared light signal. The array enables message variation spatially using a plane of infrared light emitting diodes and temporally by varying the induced phase shift. In this manner, the phase messaging array acts as the transmitter by electronically controlling the light signal phase. The ToF camera acts as the receiver by observing and recording a time-varying depth. We show a complete implementation of a 3×3 prototype array with custom hardware and demonstrating average bit accuracy as high as 97.8%. The prototype data rate with this approach is 1 Kbps that can be extended to approximately 10 Mbps.

As we are surrounded by an ever-larger variety of post-PC devices, the traditional methods for identifying and authenticating users have become cumbersome and time consuming. In this paper, we present a capacitive communication method through which a device can recognize who is interacting with it. This method exploits the capacitive touchscreens, which are now used in laptops, phones, and tablets, as a signal receiver. The signal that identifies the user can be generated by a small transmitter embedded into a ring, watch, or other artifact carried on the human body. We explore two example system designs with a low-power continuous transmitter that communicates through the skin and a signet ring that needs to be touched to the screen. Experiments with our prototype transmitter and tablet receiver show that capacitive communication through a touchscreen is possible, even without hardware or firmware modifications on a receiver. This latter approach imposes severe limits on the data rate, but the rate is sufficient for differentiating users in multiplayer tablet games or parental control applications. Controlled experiments with a signal generator also indicate that future designs may be able to achieve data rates that are useful for providing less obtrusive authentication with similar assurance as PIN codes or swipe patterns commonly used on smartphones today.

Radio frequency based device-free passive (DfP) localization techniques have shown great potentials in localizing individual human subjects, without requiring them to carry any radio devices. In this study, we extend the DfP technique to count and localize multiple subjects in indoor environments. To address the impact of multipath on indoor radio signals, we adopt a fingerprinting based approach to infer subject locations from observed signal strengths through profiling the environment. When multiple subjects are present, our objective is to use the profiling data collected by a single subject to count and localize multiple subjects without any extra effort. In order to address the non-linearity of the impact of multiple subjects, we propose a successive cancellation based algorithm to iteratively determine the number of subjects. We model indoor human trajectories as a state transition process, exploit indoor human mobility constraints and integrate all information into a conditional random field (CRF) to simultaneously localize multiple subjects. As a result, we call the proposed algorithm SCPL -- sequential counting, parallel localizing. We test SCPL with two different indoor settings, one with size 150 m2 and the other 400 m2. In each setting, we have four different subjects, walking around in the deployed areas, sometimes with overlapping trajectories. Through extensive experimental results, we show that SCPL can count the present subjects with 86% counting percentage when their trajectories are not completely overlapping. Our localization algorithms are also highly accurate, with an average localization error distance of 1.3 m.

Augmented Reality (AR) applications benefit from accurate detection of the objects that are within a person's view. Typically, it is not only desirable to identify what is currently within view, but also to navigate the users view to the item of interest - for example, finding a misplaced object. In this paper we demonstrate a low-power hybrid radio-optical beaconing system, where objects of interest are tagged with battery-powered RFID-like tags equipped with infrared light emitting diodes (LED) that emit periodic infrared beacons. These beacons are used for accurately estimating the angle and distance from the object to the receiver so as to locate it. The beacons are synchronized using the radio link that is also used to convey the object's unique ID.

Device-free passive (DfP) localization is proposed to localize human subjects indoors by observing how the subject disturbs the pattern of the radio signals without having the subject wear a tag. In our previous work, we have proposed a probabilistic classification based DfP technique, which we call PC-DfP in short, and demonstrated that PC-DfP can classify which cell (32 cells in total) is occupied by the stationary subject with an accuracy as high as 97.2% in a one-bedroom apartment. In this poster, we focus on extending PC-DfP to track a mobile subject in indoor environments by taking into consideration that a human subject's locations should form a continuous trajectory. Through experiments in a 10 × 15 meters open plan office, we show that we can achieve better accuracies by exploiting the property of continuous mobility trajectories.

Radio frequency based device-free passive localization has been proposed as an alternative to indoor localization because it does not require subjects to wear a radio device. This technique observes how people disturb the pattern of radio waves in an indoor space and derives their positions accordingly. The well-known multipath effect makes this problem very challenging, because in a complex environment it is impractical to have enough knowledge to be able to accurately model the effects of a subject on the surrounding radio links. In addition, even minor changes in the environment over time change radio propagation sufficiently to invalidate the datasets needed by simple fingerprint-based methods. In this paper, we develop a fingerprinting-based method using probabilistic classification approaches based on discriminant analysis. We also devise ways to mitigate the error caused by multipath effect in data collection, further boosting the classification likelihood.

As we are surrounded by an ever-larger variety of post-PC devices, the traditional methods for identifying and authenticating users have become cumbersome and time-consuming. In this paper, we present a capacitive communication method through which a device can recognize who is interacting with it. This method exploits the capacitive touchscreens, which are now used in laptops, phones, and tablets, as a signal receiver. The signal that identifies the user can be generated by a small transmitter embedded into a ring, watch, or other artifact carried on the human body. We explore two example system designs with a low-power continuous transmitter that communicates through the skin and a signet ring that needs to be touched to the screen. Experiments with our prototype transmitter and tablet receiver show that capacitive communication through a touchscreen is possible, even without hardware or firmware modifications on a receiver. This latter approach imposes severe limits on the data rate, but the rate is sufficient for differentiating users in multiplayer tablet games or parental control applications. Controlled experiments with a signal generator also indicate that future designs may be able to achieve datarates that are useful for providing less obtrusive authentication with similar assurance as PIN codes or swipe patterns commonly used on smartphones today.

Today's identification and authentication mechanisms for touchscreen-enabled devices are cumbersome and do not support brief usage and device sharing. To address this challenge, this work explores a novel form of "wireless" communication that exploits the capacitive touchscreens which are now used in laptops, phones, and tablets, as a signal receiver. Using a custom built hardware token, in the form of a wearable ring, we show a proof-of-concept system that transmits a user identification code to the mobile device through the touchscreen. This mechanism works without any modification to the hardware or the firmware of the mobile device.

Device-free passive localization (DfP) techniques can localize human subjects without wearing a radio tag. Being convenient and private, DfP can find many applications in ubiquitous/pervasive computing. Unfortunately, DfP techniques need frequent manual recalibration of the radio signal values, which can be cumbersome and costly. We present SenCam, a sensor-camera collaboration solution that conducts automatic recalibration by leveraging existing surveillance camera(s). When the camera detects a subject, it can periodically trigger recalibration and update the radio signal data accordingly. This technique requires camera access occasionally each month, minimizing computational costs and reducing privacy concerns when compared to localization techniques solely based on cameras. Through experiments in an open indoor space, we show that this scheme can retain good localization results while avoiding manual recalibration.

In contrast to traditional sensor networks, the "Internet of Things" focuses on interactions between humans and physical objects rather than on sensing and reporting low level information. While several middle-ware systems have been created to simplify the task of managing and aggregating data from multiple sensor networks that use different hardware and software, management of the data is not sufficient to build an Internet of Things.

In this paper, we present the design, implementation and evaluation of a RF-based device-free passive localization strategy using active RFID nodes. Patterns of the measured power on multiple radio links are used to determine the location of a person in a room in a home environment. We develop an adaptive algorithm and training technique to minimize multi-path effects. With experimental deployment in a 5 x 8 meters room, we demonstrate that our system can successfully localize an individual to a 30-inch grid square with an 97.2% accuracy and 0.36 meters average error distance.

A typical wireless sensor network consists of many small sensors that collect instrument data around their locations and forward it to a central location for data processing. These networks can be deployed to monitor livestock and agricultural assets, products in a store, patients in a hospital, and so on. In many cases sensors have to be densely deployed, and collisions or overhead due to collision avoidance will considerably degrade the system performance below an application's required levels. With the decreasing cost of radio devices the obvious solution to this problem is the use of multiple receivers on different radio channels. However, we show that if receivers can be placed in different locations then increasing the number of receivers on a single channel will increase the rate of the capture effect and decrease collision losses, while also increasing the fairness of the transmitters' radio links. Not only can this single channel approach be more effective than using multiple channels, it is also required for some techniques, such as localization, where each receiver must be able to detect a transmission from any transmitter. We also show that the optimal choice between these two solutions is influenced by the radio attenuation rate and the number of receivers in the system.

We explore the problem of detecting whether a device has moved within a room. Our approach relies on comparing summaries of received signal strength measurements over time, which we call descriptors. We consider descriptors based on the differences in the mean, standard deviation, and histogram comparison. In close to 1000 mobility events we conducted, our approach delivers perfect recall and near perfect precision for detecting mobility at a granularity of a few seconds. It is robust to the movement of dummy objects near the transmitter as well as people moving within the room. The detection is successful because true mobility causes fast fading, while environmental mobility causes shadow fading, which exhibit considerable difference in signal distributions. The ability to produce good detection accuracy throughout the experiments also demonstrates that our approach can be applied to varying room environments and radio technologies, thus enabling novel security, health care, and inventory control applications.

Asset tracking is an important application domain for wireless sensor networks. However, continuous tracking of a large number of items at the individual item level over a significant period of time is still not feasible. There are two main obstacles. The first is the need for efficient, low-power communication protocols. Many current protocols employ energy-expensive methods to achieve reliable communication for arbitrary traffic situations. Such protocols are not suitable for continuous asset tracking applications. The second challenge is the lack of a robust presence detection algorithm that can differentiate packet losses caused by a missing item from packet losses caused by the ambient radio environment. In this paper, we designed a simple communication protocol, Uni-HB, and demonstrated it can lead to longer system lifetime and higher communication reliability than several popular protocols. We also devised two robust detection algorithms that can yield low false alarm rates while achieving timely loss notification. We took an experimental approach, and evaluated protocols on a generic embedded hardware platform that has an similar architecture to motes. We also derived analytical models to validate our experimental measurements.

In this paper, we investigate one of the major challenges in pervasive systems: energy efficiency, by exploring the design of an RFID system intended to support the simultaneous and real time monitoring of thousands of entities. These entities, which may be individuals or inventory items, each carry a lowpower transmit-only tag and are monitored by a collection of networked base-stations reporting to a central database. We have built a customized transmit-only tag with a small formfactor, and have implemented a real-time monitoring application intended to verify the presence of each tag in order to detect potential disappearance of a tag (perhaps due to item theft). Throughout the construction of our pervasive system, we have carefully engineered it for extended tag lifetime and reliable monitoring capabilities in the presence of packet collisions, while keeping the tags small and inexpensive. The major challenge in this architecture (called Roll-Call™) is to supply the energy needed for long range continuous tracking for a year or more while keeping the tags (called PIPs) small and inexpensive. We have used this as a model problem for optimizing cost, size and lifetime across the entire pervasive, persistent system from firmware to protocol.

The authors discuss some of the effects quantum mechanics has on the performance of nanometer-scale devices. At low temperature, the confinement and the coherence of the electronic motion on the scale of the electron wavelength give rise to gross deviations from classical charge transport that describes the resistance found in large conventional devices. The authors examine three examples of the quantum mechanical nature of the resistance of a split-gate MODFET, that are not accounted for in conventional classical models of a FET, and yet may influence device speed, noise performance and device isolation. The authors consider the temperature and electric field ranges where quantum mechanical effects are manifested in the charge transport, and speculate about the conditions in which parasitic quantum mechanical effects might be found in a conventional device.>

An application of back-propagation networks to handwritten zip code recognition is presented. Minimal preprocessing of the data is required, but the architecture of the network is highly constrained and specifically designed for the task. The input of the network consists of size-normalized images of isolated digits. The performance on zip code digits provided by the US Postal Service is 92% recognition, 1% substitution, and 7% rejects. Structured neural networks can be viewed as statistical methods with structure which bridge the gap between purely statistical and purely structural methods.>

Hardware architectures for character recognition are discussed, and choices for possible circuits are outlined. An advanced (and working) reconfigurable neural-net chip that mixes analog and digital processing is described. It is found that different approaches to image recognition often lead to neural-net architectures that have limited connectivity and repeated use of the same set of weights. This architecture is ideal for time-multiplexing (a combined parallel-series processing) on hardware systems that would be too small to evaluate the entire network in parallel. To make this process efficient, a chip needs to have shift registers to format the input data and additional registers to store intermediate results. Within this framework, it is possible to design chips that have broad utility, large connection capacity, and high speed. This was demonstrated by a new chip with 32000 reconfigurable connections

The ability of learning networks to generalize can be greatly enhanced by providing constraints from the task domain. This paper demonstrates how such constraints can be integrated into a backpropagation network through the architecture of the network. This approach has been successfully applied to the recognition of handwritten zip code digits provided by the U.S. Postal Service. A single network learns the entire recognition operation, going from the normalized image of the character to the final classification.