Abstract: A method for performing cuffless blood pressure (BP) measurement, including: obtaining a first physiological signal and a second physiological signal associated with a user; providing the first physiological signal as an input to a first transformer model; providing the second physiological signal as an input to a second transformer model; providing an output of the first transformer model and an output of the second transformer model as inputs to a third transformer model; providing an output of the third transformer model to at least one BP estimation model; and generating an estimated BP value corresponding to the first physiological signal and the second physiological signal based on an output of the at least one BP estimation model

Abstract: A method for generating a customized speech enhancement model includes obtaining noisy-clean speech data from a source domain, obtaining noisy speech data from a target domain; obtaining raw speech data, using the noisy-clean speech data, the noisy speech data, and the raw speech data, training the customized SE model based on at least one of self-supervised representation-based adaptation (SSRA), ensemble mapping, or self-supervised adaptation loss, generating the customized SE model by denoising the noisy speech data using the trained customized SE model, and providing the customized SE model to a user device to use the denoised noisy speech data.

Abstract: A method includes accessing a training dataset having multiple samples, where each sample includes a data point for each of multiple modalities. The method also includes generating, using a first encoder associated with a first modality of the multiple modalities, first modality embeddings for data points of the first modality in the training dataset. The method further includes, for each first modality embedding, determining a similarity metric to other first modality embeddings. The method also includes generating, using a second encoder associated with a second modality of the multiple modalities, second modality embeddings for data points of the second modality in the training dataset. In addition, the method includes training the second encoder based on a contrastive loss function to align the first modality embeddings and the second modality embeddings from different samples of the training dataset, where the contrastive loss function is weighed using the similarity metrics.

Abstract: A system and method for combining computer vision information about human subjects within the field-of-view of a computer vision subsystem with RF Angle of Arrival (AoA) information from an RF receiver subsystem to locate, identify, and track individuals and their location. The RF receiver subsystem may receive RF signals emitted by one or more electronic devices (e.g., a mobile phone) carried, held, or otherwise associated with am individual. Further, gestures can be made with the device and they can be detected by the system.

Abstract: A system and method for combining computer vision information about human subjects within the field-of-view of a computer vision subsystem with RF Angle of Arrival (AoA) information from an RF receiver subsystem to locate, identify, and track individuals and their location. The RF receiver subsystem may receive RF signals emitted by one or more electronic devices (e.g., a mobile phone) carried, held, or otherwise associated with am individual. Further, gestures can be made with the device and they can be detected by the system.

Abstract: Human environments are typified by walls—homes, offices, schools, museums, hospitals and pretty much every indoor context one can imagine has walls. In many cases, they make up a majority of readily accessible indoor surface area, and yet they are static—their primary function is to be a wall, separating spaces and hiding infrastructure. We present the Wall++ system, a low-cost sensing approach that allows walls to become a smart infrastructure. Instead of merely separating spaces, walls can now enhance rooms with sensing and interactivity. Our wall treatment and sensing hardware can track users' touch and gestures, as well as estimate body pose if they are close. By capturing airborne electromagnetic noise, we can also detect what appliances are active and where they are located. Through a series of evaluations, we demonstrate the Wall++ system can enable robust room-scale interactive and context-aware applications.

Abstract: A technique for identifying individual instances of electronic devices. This is done by using a basic RFID reader to read the RF emissions from the electronic device to obtain an emitted electromagnetic spectrum and compare it to a library of emitted electromagnetic spectrums of different instances of that type of electronic device and, based on that comparison, finding a best match and identifying the electronic device as being a particular instance of that type of electronic device. This comparison may be made by computing Euclidean distances between vectors that are based on the measured electromagnetic spectrums.

Abstract: Human environments are typified by walls—homes, offices, schools, museums, hospitals and pretty much every indoor context one can imagine has walls. In many cases, they make up a majority of readily accessible indoor surface area, and yet they are static—their primary function is to be a wall, separating spaces and hiding infrastructure. We present the Wall++ system, a low-cost sensing approach that allows walls to become a smart infrastructure. Instead of merely separating spaces, walls can now enhance rooms with sensing and interactivity. Our wall treatment and sensing hardware can track users' touch and gestures, as well as estimate body pose if they are close. By capturing airborne electromagnetic noise, we can also detect what appliances are active and where they are located. Through a series of evaluations, we demonstrate the Wall++ system can enable robust room-scale interactive and context-aware applications.

Abstract: Touch-based communication between users and various electronic devices is provided by encoding data onto the device's EMI. When the device is touched by a user, the data encoded EMI signal travels through their body and into a radio receiver worn by the user, such as via a wrist band. Results show that electronic primitives such as LEDs, buttons, I/O lines, LCD screens, motors and power supplies can be turned into radio transmitters capable of touch communication. Effective data rates range from 5.8 Kbps to 22 bit per image depending on the primitive used.

Abstract: Touch-based communication between users and various electronic devices is provided by encoding data onto the device's EMI. When the device is touched by a user, the data encoded EMI signal travels through their body and into a radio receiver worn by the user, such as via a wrist band. Results show that electronic primitives such as LEDs, buttons, I/O lines, LCD screens, motors and power supplies can be turned into radio transmitters capable of touch communication. Effective data rates range from 5.8 Kbps to 22 bit per image depending on the primitive used.

Abstract: An object recognition device that senses electrical signals conducted by the body of a human user (e.g., as a result of direct contact with or close proximity to a device emitting or conducting electromagnetic noise), compares the sensed electrical signals to a plurality of signatures of electrical signals produced by a corresponding plurality of types of electrical and electromechanical devices to determine the type of electrical or electromechanical device that generated the electrical signals sensed by the sensor, and communicates information to the human user related to or triggered by the electrical or electromechanical device.

Abstract: A technique for identifying individual instances of electronic devices. This is done by using a basic RFID reader to read the RF emissions from the electronic device to obtain an emitted electromagnetic spectrum and compare it to a library of emitted electromagnetic spectrums of different instances of that type of electronic device and, based on that comparison, finding a best match and identifying the electronic device as being a particular instance of that type of electronic device. This comparison may be made by computing Euclidean distances between vectors that are based on the measured electromagnetic spectrums.

Abstract: An object recognition device that senses electrical signals conducted by the body of a human user (e.g., as a result of direct contact with or close proximity to a device emitting or conducting electromagnetic noise), compares the sensed electrical signals to a plurality of signatures of electrical signals produced by a corresponding plurality of types of electrical and electromechanical devices to determine the type of electrical or electromechanical device that generated the electrical signals sensed by the sensor, and communicates information to the human user related to or triggered by the electrical or electromechanical device.

Abstract: A method and system for providing Wi-Fi positioning. The method includes transmitting multiple messages by an electronic device. A received signal is reconstructed using the multiple messages by another electronic device. Each message of the multiple messages is arranged in a particular order for reconstructing the received signal through a determination of relative time difference between the multiple messages.