Contact

CSE 568
gshyamcs.washington.edu
Areas of interest: 

Computational health, AI for sound, networks, bio-robotics, wireless, mobile and ubiquitous computing, sensing, security and privacy

Tympanometry for middle ear diagnosis using smartphones

Middle ear disorders are one of the most common causes of preventable hearing loss. Unfortunately, while the developing world bears a disproportionate burden of these disorders, it often lacks access to diagnostic tools.


Wind dispersal of battery-free devices for planetary-scale environmental sensing

Plants cover a large fraction of the Earth’s land mass despite most species having limited to no mobility. Many plants have evolved mechanisms to disperse their seeds using the wind. A dandelion seed, for example, can travel as far as a kilometer in dry, windy, and warm conditions. Inspired by this, we demonstrate wind dispersal of battery-free wireless sensing devices. Our millimeter-scale devices are designed on a flexible substrate using programmable, off-the-shelf parts to enable scalability and flexibility for various sensing and computing applications.

Laser speckle using smartphone LiDAR

We present the first system to determine fluid properties using the LiDAR sensors present on modern smartphones. Traditional methods of measuring properties like viscosity require expensive laboratory equipment or a relatively large amount of fluid. In contrast, our smartphone-based method is accessible, contactless and works with just a single drop of liquid. Our design works by targeting a coherent LiDAR beam from the phone onto the liquid.

Blood coagulation testing using smartphones

Frequent blood clot testing is critical for millions of people on lifelong anticoagulation with warfarin. Currently, testing is performed in hospital laboratories or with expensive point-of-care devices limiting the ability to test frequently and affordably. We report a proof-of-concept blood clot testing system that uses the vibration motor and camera on smartphones to track micro-mechanical movements of a copper particle.

AI for contactless cardiology

Cardiology as a field has seen phenomenal technological advances over the last few decades. A number of tools in cardiology however require sensors and/or electrodes on the human body to capture the cardiac signals and diagnose patients. In this project, we aim to create medical tools that use smartphones and smart speakers to contactlessly detect cardiac conditions. We present a proof-of-concept system for acquiring individual heart beats using smart speakers in a fully contact-free manner.

Deep learning for directional hearing

On-device directional hearing requires audio source separation from a given direction while achieving stringent human-imperceptible latency requirements. While neural networks can achieve significantly better performance than traditional beamformers, all existing models fall short of supporting low-latency causal inference on computationally-constrained wearables. We present DeepBeam, a hybrid model that combines traditional beamformers with a custom lightweight neural network.

AllSee

Existing gesture-recognition systems consume significant power and computational resources that limit how they may be used in low-end devices. We introduce AllSee, the first gesture-recognition system that can operate on a range of computing devices including those with no batteries. AllSee consumes three to four orders of magnitude lower power than state-of-the-art systems and can enable always-on gesture recognition for smartphones and tablets.

Ambient Backscatter

As computing devices become smaller and more numerous, powering them becomes more difficult; wires are often not feasible, and batteries add weight, bulk, cost, and require recharging/replacement that is impractical at large scales. Ambient backscatter communication solves this problem by leveraging existing TV and cellular transmissions, rather than generating their own radio waves. This novel technique enables ubiquitous communication where devices can communicate among themselves at unprecedented scales and in locations that were previously inaccessible.

WiSee

WiSee is a novel interaction interface that leverages ongoing wireless transmissions in the environment (e.g., WiFi) to enable whole-home sensing and recognition of human gestures. Since wireless signals do not require line-of-sight and can traverse through walls, WiSee can enable whole-home gesture recognition using few wireless sources (e.g., a Wi-Fi router and a few mobile devices in the living room).

MIMO Cognitive Systems

This project presents TIMO, the first WiFi receiver that can decode in the presence of high-power unknown cross-technology interference. Traditionally, researchers have addressed cross-technology interference by dynamically searching for and switching to unused frequencies. We approach the problem from a different perspective: can one operate in already used frequencies in the presence of potentially unknown interferers? We build a new cognitive framework for the unlicensed band which allows multi-antenna devices to work in used frequencies.


Random Access MIMO Networks

This project presents 802.11n+, the first fully distributed random access protocol that allows nodes to contend not just for time, but also the concurrent transmissions supported by multiple antennae. In n+, even when the medium is occupied, nodes with more antennae can transmit concurrently without harming ongoing transmissions. Furthermore, such nodes can contend for the medium in a fully distributed way. Our testbed evaluation shows that even for a small network with three competing node pairs, the resulting system about doubles the average network throughput.


Password-Free Wireless Security

This project presents tamper-evident pairing, the first wireless pairing protocol that works in-band, with no pre-shared keys, and protects against MITM attacks. The main innovation is a new key exchange message constructed in a manner that ensures an adversary can neither hide the fact that a message was transmitted, nor alter its payload without being detected. Thus, any attempt by an adversary to interfere with the key exchange translates into the pairing devices detecting either invalid pairing messages or an unacceptable increase in the number of such messages.


Securing Medical Implants

Wireless communication has become an intrinsic part of modern implantable medical devices (IMDs). Recent work, however, has demonstrated that wireless connectivity can be exploited to compromise the confidentiality of IMDs’ transmitted data or to send unauthorized commands to IMDs—even commands that cause the device to deliver an electric shock to the patient. The key challenge in addressing these attacks stems from the difficulty of modifying or replacing already-implanted IMDs.


Practical Interference Alignment

This is the first work to demonstrate the practicality of interference alignment, which was considered to be a purely theoretical concept prior to our work. Further, we synthesize interference alignment and interference cancellation, showing that the combination is beneficial in scenarios where neither interference alignment nor cancellation applies alone. Our implementation on software radios demonstrate that for 2-antenna MIMO networks, IAC increases the average throughput by 1.5x on the downlink and 2x on the uplink.


ZigZag Decoding

Collisions are a known problem in wireless networks. The existing approaches address this problem by designing techniques that avoid collisions. This project takes an alternate approach: Instead of trying to avoid collisions, lets embrace collisions. In particular, we present ZigZag decoding, a new WiFi receiver that decodes collisions. The core contribution of ZigZag is a new interference cancellation technique that does not make any assumptions of synchronization, large differences in power, or special codes.