Millimeter wave (mmWave) networks offer multi-gigabit data rates but suffer from severe path loss and blockage, resulting in spotty coverage. Emerging reconfigurable intelligent surfaces (RIS) can mitigate these challenges, but their reliance on active control channels, power sources, and complex runtime coordination imposes significant hardware and deployment overhead. This paper introduces FlowForm, a system that expands mmWave coverage using networks of passive metasurfaces that require no power, control, or runtime coordination. FlowForm's key innovation is a hierarchical flow topology that organizes passive metasurfaces into major flows (directional relay chains using near-field focusing) and minor flows (wide-area fan beams), enabling multi-hop passive routing and over-the-air combination of analog signals. We develop a theoretical framework establishing the optimality of this topology and a hierarchical optimization algorithm that jointly determines metasurface placement and beam configurations. FlowForm operates transparently with standard mmWave network protocols, managing channel dynamics and multi-user interference through diversity-aware design rather than runtime reconfiguration. Our experimental evaluation across five indoor environments demonstrates up to 94% average rate improvement and 114% coverage expansion using low-cost 3D-printed metasurfaces ($2 per unit), achieving performance comparable to active RIS at orders of magnitude lower cost.

Human external biofluid (e.g., sweat, urine) contains vast health data that is readily harvestable. Currently, wearable sweat sensors require an electrochemical-based approach that is used in single use, creating environmental pollution as people track their exercise in the wild. Moreover, such solution relies on a battery-powered design, which brings battery health and thermal related issues. We present MetaBioLiq, a 3D printed wireless-readable sweat sensing system that offers continuous monitoring, featuring completely passive, environmentally friendly, and easily accessible. MetaBioLiq is developed upon sweat liquid's resonance upon high frequency RF interaction, with different sweat content driving RF resonance characteristics. To activate such resonance, we design 3D PolyLactic Acid (PLA) structures that capture e-field energy from the air, and tunneling it to the sweat. Once the resonance effect occurs, we analyze return signal from a wireless RF receiver to decouple the sweat's resonance. Lastly, we evaluate MetaBioLiq's performance with 24 artificial sweat samples containing different levels of glucose, electrolytes, and fat. MetaBioLiq proves its effectiveness with 95% liquid level detection performance, and 96% sweat liquid identification performance. We further investigate MetaBioLiq's robustness and reliability, as well as limitations. Overall, MetaBioLiq shows promising results to expand the realm of mobile continuous sensing to microscopic realm untangible in the past.

Soil moisture level sensing is essential for enabling smart irrigation, which is crucial for our food security and sustainable agriculture. Existing soil moisture sensing systems face limitations such as single-layer sensing, limited depth, power supply reliance, and complex calibration. In addition, costly and cumbersome sensor unit design hinders mass and dense deployment of passive intelligence. This paper introduces MetaSoil, a soil moisture sensing system that is calibration-free, continuous, and multi-layered, leveraging a passive 3D printable mmWave metamaterial. When soil moisture level changes, our hydrogel patched polylactic acid (PLA) metamaterial alters resonant frequency in the impinging mmWave signals due to impedance match offset. Our system eliminates in-soil power supply dependencies by utilizing the RF resonance of 3D-printed metamaterial, allowing for deeper placement, and simultaneous multi-layer sensing. We then integrate a commercial-off-the-shelf (COTS) mmWave radar to query the metamaterial sensor. With MetaSoil's fully passive metamaterial pole, RF signal from far is redirected towards the sensor unit, bypassing soil's heavy attenuation effect. Through our extensive evaluation, MetaSoil achieves 98.9 % accuracy with ±10% moisture level precision in single-layered sensing, at depth of 1m meter. It achieves 98.8 % accuracy with ±10% in double layered sensing at same depth with 10cm sensor spacing. We further examine the robustness of our system with real-world requirements. Overall, MetaSoil represents a low-cost, durable, and easily deployable solution that supports remote and continuous soil moisture monitoring, advancing the scalability and effectiveness of smart agricultural practices.

Reliable cross medium (e.g., air-water) communication using radio frequency (RF) has remained an open-problem for decades. Currently, underwater devices cannot communicate directly with land-based or airborne devices. Typical solutions are inadequate when communicating through the boundary due to cross-medium boundary reflection/refraction/attenuation effects. We present MetaLink, an RF wireless communication system that enables underwater radios to communicate with airborne ones using novel underwater antenna design and 3D printed bianisotropic metasurface. MetaLink leverages bianisotropic structures that can correct for the severe boundary reflections/refractions between the air/water mediums, opening up the air/water medium as a viable communication channel without the need for multiple types of signals. We further exploit the electromagnetic properties of water to drastically scale down MetaLink's meta-atom size, and improve communication range. To examine real world communications performance from water to air, we prototype MetaLink and measure in a 14 ft deep swimming pool. Moreover, we push the robustness, reliability, and performance of MetaLink to its limit under various real-world circumstances. Our experiments demonstrate that MetaLink can communicate through the water/air boundary with SNR improvements of more than 35dB using WiFi modulation at distances of 14 ft and reach a simulated maximum of 95 ft within water using commercially available equipment and measured data.

Temperature sensor is one of the most widespread technologies in the IoT era. Wireless temperature monitoring systems are convenient to deploy and can drive mass applications in the fields of smart home, transportation and logistics. Currently, wireless temperature monitoring products are based on microelectronic and semiconductor components, which are not cost-effective (e.g., a few dollars) and more importantly, generate electronic wastes. In this work, we present ThermoWave, a new paradigm of wireless temperature monitoring that is ecological, battery-less, and ultra-low cost. Specifically, ThermoWave is on the basis of the thermal scattering effect on millimeter-wave (mmWave) signals. Specifically, cholesteryl materials align their molecular patterns at different environmental temperatures, and this temperature-induced pattern change will be modulated and sensed by the scattered mmWave signals. There are three functional modules in the ThermoWave system. The ThermoTag is a cholesteryl material inked film or paper tag that can be conveniently attached to the object of interest to monitor temperature changes. Each ThermoTag costs less than 0.01 dollars. The temperature modulated mmWave scattering will be received by a mmWave-radar based ThermoScanner and demodulated by a software-based temperature decoder ThermoSense, which includes a model-based method (i.e., ThermoDot) for point temperature estimation and a data-driven method (i.e., ThermoNet) for thermal imaging. We prototype and evaluate the ThermoWave system performance in both controlled and real-world setups. Experimental results show that the ThermoWave achieves the precision of ±1.0°F in the range of 30°F to 120°F in a controlled setup. We also investigate the performance in real-world applications, and the ThermoWave can reach the ±3.0°F precision in the temperature estimation. We also test and discuss sustainability, durability, robustness, and cost-effectiveness of the ThermoWave in both design and experiments.

Approximately 7 million survivors of stroke reside in the United States. Over half of these individuals will have residual deficits, making stroke one of the leading causes of disability. Long-term rehabilitation opportunities are critical for millions of individuals with chronic upper limb motor deicits due to stroke. Traditional in-home rehabilitation is reported to be dull, boring, and un-engaging. Moreover, existing rehabilitation technologies are not user-friendly and cannot be adaptable to different and ever-changing demands from individual stroke survivors. In this work, we present RehabPhone, a highly-usable software-defined stroke rehabilitation paradigm using the smartphone and 3D printing technologies. This software-definition has twofold. First, RehabPhone leverages the cost-effective 3D printing technology to augment ordinal smartphones into customized rehabilitation tools. The size, weight, and shape of rehabilitation tools are software-defined according to individual rehabilitation needs and goals. Second, RehabPhone integrates 13 functional rehabilitation activities co-designed with stroke professionals into a smartphone APP. The software utilizes built-in smartphone sensors to analyzes rehabilitation activities and provides real-time feedback to coach and engage stroke users. We perform the in-lab usability optimization with the RehabPhone prototype with involving 16 healthy adults and 4 stroke survivors. After that, we conduct a 6-week unattended intervention study in 12 homes of stroke residence. In the course of the clinical study, over 32,000 samples of physical rehabilitation activities are collected and evaluated. Results indicate that stroke users with RehabPhone demonstrate a high adherence and clinical efficacy in a self-managed home-based rehabilitation course. To the best of our knowledge, this is the first exploratory clinical study using mobile health technologies in real-world stroke rehabilitation.

Core body temperature is an important indicator of medical treatment. Sudden changes in core body temperature can be a precursor to neurodegenerative diseases such as Parkinson's disease. These diseases have the potential to strike at any time, therefore, long-term monitoring of core body temperature and alerting to sudden changes in temperature become important. In this paper, we designed an in-ear thermometer to monitor the core body temperature with the help of smartphone.

Digital screens, such as liquid crystal displays (LCDs), are vulnerable to attacks (e.g., "shoulder surfing") that can bypass security protection services (e.g., firewall) to steal confidential information from intended victims. The conventional practice to mitigate these threats is isolation. An isolated zone, without accessibility, proximity, and line-of-sight, seems to bring personal devices to a truly secure place.In this paper, we revisit this historical topic and re-examine the security risk of screen attacks in an isolation scenario mentioned above. Specifically, we identify and validate a new and practical side-channel attack for screen content via liquid crystal nematic state estimation using a low-cost radio-frequency sensor. By leveraging the relationship between the screen content and the states of liquid crystal arrays in displays, we develop WaveSpy, an end-to-end portable through-wall screen attack system. WaveSpy comprises a low-cost, energy-efficient and light-weight millimeter-wave (mmWave) probe which can remotely collect the liquid crystal state response to a set of mmWave stimuli and facilitate screen content inference, even when the victim’s screen is placed in an isolated zone. We intensively evaluate the performance and practicality of WaveSpy in screen attacks, including over 100 different types of content on 30 digital screens of modern electronic devices. WaveSpy achieves an accuracy of 99% in screen content type recognition and a success rate of 87.77% in Top-3 sensitive information retrieval under real-world scenarios, respectively. Furthermore, we discuss several potential defense mechanisms to mitigate screen eavesdropping similar to WaveSpy.

Digital devices have become a necessity in our daily life, with digital screens acting as a gateway to access a plethora of information present in the underlying device. However, these devices emit visible light through screens where long-term use can lead to significant screen exposure, further influencing users' health. Conventional methods on screen exposure detection (\textite.g., photo logger) are usually privacy-invasive and expensive, further, require ideal light conditions, which are unattainable in real practice. Considering the light intensity and spectrum vary among different light sources, an effective screen spectrum estimation can provide vital information about screen exposure. To this end, we first investigate the characteristics of the junction between p-type and n-type semiconductor (i.e., PN junction) to sense the spectrum under various conditions. Empirically, we design and implement, \textsfSpecEye, an end-to-end, low cost, wearable, and privacy-preserving screen exposure detection system with a mobile application. For validating the performance of our system, we conduct comprehensive experiments with $54$ commodity digital screens, at $43$ distinct locations, with results showing a base accuracy of $99$%, and an equal error rate (EER) approaching $0.80$% under the controlled lab setup. Moreover, we assess the reliability, robustness, and performance variation of \textsfSpecEye under various real-world circumstances to observe a stable accuracy of $95$%. Our real-world study indicates \textsfSpecEye is a promising system for screen exposure detection in everyday life.

Hidden electronics possess the risk of both security threat and privacy intrusion. We present a wireless hidden electronic recognition system, through electronic components unique mmWave nonlinear responses to identify the threats. We then evaluate E-Eye's performance and robustness with a controlled experiment and a field study using iconic devices and score the system with metrics. Results prove that E-Eye is an accurate and robust hidden electronic recognition system.

Inventory management is pivotal in the supply chain to supervise the non-capitalized products and stock items. Item counting, indexing and identification are the major jobs of inventory management. Currently, the most adopted inventory technologies in product counting/identification are using either the laser-scannable barcode or the radio-frequency identification (RFID). However, the laser-scannable barcode is entangled by an alignment issue (i.e., the laser reader must align with one barcode in line-of-sight), and the RFID is economically and environmentally unfriendly (i.e., high-cost and not naturally disposable). To this end, we propose FerroTag which is a paper-based mmWave-scannable tagging infrastructure for the next generation inventory management system, featuring ultra-low cost, environment-friendly, battery-free and in-situ (i.e., multiple tags can be simultaneously processed outside the line-of-sight). FerroTag is developed on top of the FerroRF effects. Specifically, the magnetic nanoparticles within the ferrofluidic ink reply to probing mmWave with classifiable features (i.e., the FerroRF response). By designating the ink pattern and hence the location of particles, the related FerroRF response can be modified. Thus, a specifically designated ferrofluidic ink printed pattern, which is associated with a unique FerroRF response, is a remotely retrievable (a.k.a., mmWave-scannable) identity. Furthermore, we augment FerroTag by designing a high capacity pattern system and a fine-grained identification protocol such that the capacity and robustness of FerroTag can be systematically improved in mass product management in inventory. Last but not least, we evaluate the performance of FerroTag with 201 different tag design patterns. Results show that FerroTag can identify tags with an accuracy of more than 99% in a controlled lab setup. Moreover, we examine the reliability, robustness and performance of FerroTag under various real-world circumstances, where FerroTag maintains the accuracy over 97%. Therefore, FerroTag is a promising tagging infrastructure for the applications in inventory management systems.

I put forward a new interpolation called Adams-Bashforth interpolation of NURBS (Non-Uniform Rational B-Splines) curve in order to realize higher precision under the condition of satisfying the speed demand. IN this method, I calculated next parameter point by Adams-Bashforth equation and I used difference equation instead of derivation. I simplified and approximated the interpolation algorithm so that the speed and precision of interpolation could be ensured. Finally, we conducted simulations on this interpolation based on MATLAB and compared it with Taylor expansion algorithm so that we could prove its feasibility and correctness. We can conclude that it has a higher precision.