Multi-static backscatter networks (BNs) are strong candidates for joint communication and localization in the ambient IoT paradigm for 6G. Enabling real-time localization in large-scale multi-static deployments with thousands of devices require highly efficient algorithms for estimating key parameters such as range and angle of arrival (AoA), and for fusing these parameters into location estimates. We propose two low-complexity algorithms, Joint Range-Angle Clustering (JRAC) and Stage-wise Range-Angle Estimation (SRAE). Both deliver range and angle estimation accuracy comparable to FFT- and subspace-based baselines while significantly reducing the computation. We then introduce two real-time localization algorithms that fuse the estimated ranges and AoAs: a maximum-likelihood (ML) method solved via gradient search and an iterative re-weighted least squares (IRLS) method. Both achieve localization accuracy comparable to ML-based brute force search albeit with far lower complexity. Experiments on a real-world large-scale multi-static testbed with 4 illuminators, 1 multi-antenna receiver, and 100 tags show that JRAC and SRAE reduce runtime by up to 40X and IRLS achieves up to 500X reduction over ML-based brute force search without degrading localization accuracy. The proposed methods achieve 3 m median localization error across all 100 tags in a sub-6GHz band with 40 MHz bandwidth. These results demonstrate that multi-static range-angle estimation and localization algorithms can make real-time, scalable backscatter localization practical for next-generation ambient IoT networks.

We applied qSV-OCT to quantify vascular remodeling in hydrogel-treated mice wounds. Stiff gels supported stable vascular activity, while soft gels showed transient responses, demonstrating the potential of our non-invasive method to monitor and guide treatments.

Monitoring hydrogel-guided wound healing with high spatial resolution without invasive procedures remains a key clinical challenge. We developed an integrated platform combining optical coherence tomography (OCT) with artificial intelligence (AI) to monitor biomaterial-modulated tissue regeneration quantitatively. Using AI-assisted 3D segmentation and enhanced speckle variance OCT, we tracked longitudinal changes in granulation tissue, dermal restoration, and vascular remodeling in wounds treated with hydrogels of tunable stiffness. This multimodal OCT-AI platform enabled quantitative characterization of stiffness-dependent differences in wound healing progress and trajectories. Stiff hydrogels promoted earlier vascular remodeling and accelerated healing, whereas soft hydrogels exhibited delayed phase transitions and slower overall healing. These OCT-derived measurements were well-aligned with histological and immunofluorescence analyses, confirming earlier inflammation resolution in stiff hydrogel-treated wounds and validating the accuracy and sensitivity of this non-invasive approach. This multimodal OCT-AI platform shows strong potential for preclinical treatment evaluation and clinical wound monitoring.

This study introduces a diamond-based vector magnetometer that leverages Nitrogen-Vacancy (NV) centers and an advanced 3D magnetic flux concentrator (MFC) system. The MFC system amplifies and channels external magnetic fields into the quantum NV sensor while maintaining the vector properties of the magnetic field. The design incorporates multiple orthogonal pairs of triangulars, high-permeability MFC modules hosted by a polymeric 3D-printed scaffold, enabling efficient field concentration within a compact form factor. This multi-axis MFC-pair configuration effectively resolves ODMR spectral congestion, facilitating reliable detection of multiple ODMR peak pairs essential for vector-type magnetic detectors operating in the μT magnetic field range. Simulations and experimental results show that the MFCs enhances the magnetic field within the diamond interrogation volume by 144.5 with the gap between the MFC pair of 0.42 mm. This work underscores the potential of engineered 3D MFC systems to enhance magnetic vector sensitivity, offering practical solutions for applications such as navigation, anomaly detection, and biomedical sensing.

We demonstrate the application of quantified speckle variance OCT to track angiogenesis in hydrogel-treated mice wounds. Compared to traditional SV algorithms, this technique enhances vascular imaging, offering improved monitoring and optimization of wound therapies.

Optical coherence tomography (OCT) is a powerful non-invasive imaging technique, providing high-resolution three-dimensional images of biological tissues. However, accurately segmenting regions-of-interest from large 3D images is challenging and often relies on manual processes that are labor-intensive, subjective, and prone to inter-operator variability. This is particularly true in dynamic processes like wound healing, where tissue structures continuously evolve. This study introduces a U-Net neural network model for automatic segmentation of 3D OCT images from mouse wound models treated with dextran-based hydrogels. The network achieves 85.5% per-pixel validation accuracy in identifying eight structural subtypes, with the same model operating across the entire 14-day healing period. This approach enabled longitudinal in vivo monitoring of hydrogel volume and degradation, providing valuable insights into wound healing dynamics and treatment efficiency without the need for invasive biopsies.

Optical coherence tomography (OCT) is a powerful 3D imaging technique commonly used for assessment of retinal pathologies. The high cost and large form-factor of most OCT systems, however, limit its widespread adoption for applications outside of in-clinic ophthalmology, especially in low-resource environments. Advances in optoelectronic integration technology has inspired several chip-scale OCT implementations that could potentially bridge this gap by providing a low-cost, miniaturized, and easy- to-use OCT system for widespread adoption. However, to date these systems have failed to simultaneously show high sensitivity, high imaging rate, and module-level integration. In addition, no previous chip-scale OCT implementation has operated at 1060 nm - a key wavelength range for ophthalmic imaging. In this work, we utilize our heterogeneous integrated swept-source OCT platform to demonstrate high-performance imaging at 1310 nm and for the first time demonstrate high-sensitivity imaging at 1060 nm. We use hybrid integration techniques to package our OCT engine module (2 x 1 x 0.3 cm(3)), which includes a Planar Lightwave Circuit (PLC) photonic chip, integrated balanced photodiodes, collimating ball lens, and co-packaged electrothermally actuated MEMS mirror. The imaging performance of our system is validated with sensitivity measurements, 2D and 3D scans. We report record high sensitivity of 99.6 dB at 880 mu W sample power with our 1310 nm OCT chip and a sensitivity of 103.2 dB at 2 mW using our 1060 nm version, both at 100 kHz swept-laser repetition rate. These results serve as a major step towards the creation of a low-cost and miniaturized OCT system for widespread application beyond in-clinic ophthalmology.

Backscatter radio is a promising technology for low-cost and low-power Internet-of-Things (IoT) networks. The conventional monostatic backscatter radio is constrained by its limited communication range, which restricts its utility in wide-area applications. An alternative bi-static backscatter radio architecture, characterized by a dis-aggregated illuminator and receiver, can provide enhanced coverage and, thus, can support wide-area applications. In this paper, we analyze the scalability of the bi-static backscatter radio for large-scale wide-area IoT networks consisting of a large number of unsynchronized, receiver-less tags. We introduce the Tag Drop Rate (TDR) as a measure of reliability and develop a theoretical framework to estimate TDR in terms of the network parameters. We show that under certain approximations, a small-scale prototype can emulate a large-scale network. We then use the measurements from experimental prototypes of bi-static backscatter networks (BNs) to refine the theoretical model. Finally, based on the insights derived from the theoretical model and the experimental measurements, we describe a systematic methodology for tuning the network parameters and identifying the physical layer design requirements for the reliable operation of large-scale bi-static BNs. Our analysis shows that even with a modest physical layer requirement of bit error rate (BER) 0.2, 1000 receiver-less tags can be supported with 99.9% reliability. This demonstrates the feasibility of bi-static BNs for large-scale wide-area IoT applications.

Optical coherence tomography angiography (OCTA) holds promise as a non-invasive, label-free technique for visualizing blood vessels and monitoring angiogenesis. However, the traditional speckle variance (SV) algorithm, an intensity-based OCTA algorithm, cannot distinguish between variance in signal intensity caused by red blood cell movement and those arising from system noise (e.g., shot noise). This limitation results in poor image contrast in angiography images. As imaging depth increases, signal attenuation due to scattering and absorption becomes more pronounced, making it difficult to visualize deeper blood vessels. The traditional SV algorithm fails to adequately address depth- and intensity-dependent noise, leading to reduced image contrast with increasing depth. In this work, we used a quantified speckle variance (qSV) algorithm that effectively mitigates the influence of system noise and enhances the contrast of regions exhibiting motion. The qSV algorithm normalizes the SV intensity relative to system noise, allowing a quantitative assessment of intensity fluctuations due to motion above the noise floor. The algorithm's effectiveness in maintaining high image contrast at greater depths is demonstrated using a phantom flow system of intralipid flowing through a thick tube embedded in gel. Furthermore, the algorithm is applied to human blood flow data, revealing high contrast even for blood vessels located deeper within the dermal layer. This highlights the algorithm's potential for improved visualization of deeper vascular structures.

Using OCT and advanced image processing, we demonstrate a quantitative method to monitor wound healing in hydrogel-treated mouse models. Our approach enables an objective assessment of tissue regeneration and treatment optimization for improving clinical outcomes.

Many parts of human body generate internal sound during biological processes, which are rich sources of information for understanding health and wellbeing. Despite a long history of development and usage of stethoscopes, there is still a lack of proper tools for recording internal body sound together with complementary sensors for long term monitoring[1]. In this paper, we show our development of a wearable electronic stethoscope, coined "Patchkeeper" (PK), that can be used for internal body sound recording over long periods of time. PK also integrates several state-of-the-art biological sensors, including electrocardiogram (ECG), photoplethysmography (PPG), and inertial measurement unit (IMU) sensors. As a wearable device, PK can be placed on various parts of the body to collect sound from particular organs, including heart, lung, stomach, and joints etc. We show in this paper that several vital signals can be recorded simultaneously with high quality. As PK can be operated directly by the user, e.g. without involving health care professionals, we believe it could be a useful tool for telemedicine and remote diagnostics.

We demonstrate in vivo monitoring of wound healing dynamics in mice treated with hydrogels of varying stiffness using OCT. Distinct cellular responses were visualized and tracked, revealing the potential of OCT for optimizing hydrogel-based treatments.

Being able to assess dog personality can be used to, for example, match shelter dogs with future owners, and personalize dog activities. Such an assessment typically relies on experts or psychological scales administered to dog owners, both of which are costly. To tackle that challenge, we built a device called "Patchkeeper" that can be strapped on the pet's chest and measures activity through an accelerometer and a gyroscope. In an in-the-wild deployment involving 12 healthy dogs, we collected 1300 hours of sensor activity data and dog personality test results from two validated questionnaires. By matching these two datasets, we trained ten machine-learning classifiers that predicted dog personality from activity data, achieving AUCs in [0.63-0.90], suggesting the value of tracking the psychological signals of pets using wearable technologies.

Surface electromyography (sEMG) is a non-invasive method of measuring neuromuscular potentials generated when the brain instructs the body to perform both fine and coarse locomotion. This technique has seen extensive investigation over the last two decades, with significant advances in both the hardware and signal processing methods used to collect and analyze sEMG signals. While early work focused mainly on medical applications, there has been growing interest in utilizing sEMG as a sensing modality to enable next-generation, high-bandwidth, and natural human-machine interfaces. In the first part of this review, we briefly overview the human skeletomuscular physiology that gives rise to sEMG signals followed by a review of developments in sEMG acquisition hardware. Special attention is paid towards the fidelity of these devices as well as form factor, as recent advances have pushed the limits of user comfort and high-bandwidth acquisition. In the second half of the article, we explore work quantifying the information content of natural human gestures and then review the various signal processing and machine learning methods developed to extract information in sEMG signals. Finally, we discuss the future outlook in this field, highlighting the key gaps in current methods to enable seamless natural interactions between humans and machines.

The rise of IoT and 5G-powered cybernetic connectivity has drastically transformed our living environment with the rapid introduction of diverse and disaggregated sensing systems. A plethora of prototyping boards and development kits offering disparate environmental condition sensors have become available to promote DIY applications, but they require significant user programming and assembly. The adoption of commercial products on the other hand is largely limited by the high costs associated with device capability, installation and maintenance. These expenses are dominated by the power and connectivity requirements as well as long-term upkeep of device operations. To overcome these challenges, we developed an ultra-compact, low-cost and a fully packaged environmental sensing device capable of monitoring up to seven parameters at once that can be easily installed on any surface and recharges automatically with embedded solar energy harvesting. We believe this compact and power-efficient sensing device will provide an unobtrusive and cost-effective example for future distributed environment monitoring systems with a wide variety of applications.

Electrothermally actuated MEMS mirrors are significantly lower in cost than their electrostatically actuated counterparts, largely due to their ability to perform well without hermetic packaging. However, their typical slower speeds, higher power requirement, and non-linear drive limit their widespread use. In this work, we address these limitations and achieve an electrothermally actuated MEMS mirror capable of reliable linear raster scanning at speeds up to 300 Hz with a large angular range of motion ( ± 40 ∘ optical). A simple pulse design technique is used to achieve ∼ 99 % scan linearity and correct for artifacts like overshoot and ringing. Furthermore, segmented polysilicon microheaters along the actuators are used for impedance matching to low-voltage electronics, resulting in lower power consumption and improved scan speed and angular range. These mirrors serve as an attractive option for low-cost and compact integrated beam-steering optical devices, and we demonstrate one such use case in an optical coherence tomography (OCT) system.

Stress is often considered the 21 st century's epidemic, affecting more than a third of the globe's population. Long-term exposure to stress has significant side effects on physical and mental health. In this work we propose a methodology for detecting stress using abdominal sounds. For this study, eight participants were either exposed to a stressful (Stroop test) or a relaxing (guided meditation) stimulus for ten days. In total, we collected 104 hours of abdominal sounds using a custom wearable device in a belt form-factor. We explored the effect of various features on the binary stress classification accuracy using traditional machine learning methods. Namely, we observed the impact of using acoustic features on their own, as well as in combination with features representing current mood state, and hand-crafted domain-specific features. After feature extraction and reduction, by utilising a multilayer perceptron classifier model we achieved 77% accuracy in detecting abdominal sounds under stress exposure. Clinical relevance— This feasibility study confirms the link between the gastrointestinal system and stress and uncovers a novel approach for stress inference via abdominal sounds using machine learning

We developed a modular approach for biochemical sensing using aptamer-based microparticles and swept-source OCT. Tuning the crosslinker composition of the microparticle enabled varying physicochemical responses to a target biochemical, ranging from degradation to reversible swelling, which was spatiotemporally monitored in tissue-mimics using OCT.

We demonstrate the phase stability of a fully integrated chip-scale OCT interferometer for clinical otology applications, capable of imaging sub-nanometer vibrations in the middle ear and mapping the tympanic membrane vibrational modes.

By using a computer keyboard as a finger recording device, we construct the largest existing dataset for gesture recognition via surface electromyography (sEMG), and use deep learning to achieve over 90 reconstructing typed text entirely from measured muscle potentials. We prioritize the temporal structure of the EMG signal instead of the spatial structure of the electrode layout, using network architectures inspired by those used for real-time spoken language transcription. Our architecture recognizes the rapid movements of natural computer typing, which occur at irregular intervals and often overlap in time. The extensive size of our dataset also allows us to study gesture recognition after synthetically downgrading the spatial or temporal resolution, showing the system capabilities necessary for real-time gesture recognition.